Systems and methods for sequence mimicking

ABSTRACT

Embodiments of the current disclosure provide for an apparatus for sequence mimicking. The apparatus includes a historic sequence interpretation circuit, a mimicking circuit, and a sequence data provisioning circuit. The historic sequence interpretation circuit is structured to interpret historical sequence data corresponding to a sequence designed, in part, by an entity. The mimicking circuit is structured to: extract a sequence trend from the historical sequence data; identify a portion of the historical sequence data corresponding to the extracted sequence trend; and generate sequence data based at least in part on the identified portion. The sequence data provisioning circuit is structured to transmit the sequence data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to and the benefit of U.S. ProvisionalPatent Application Ser. No. 63/389,822, filed Jul. 15, 2022 and entitled“SYSTEMS AND METHODS FOR AGGLOMERATE NETWORKS” (UKGP-0012-P01).

All of the above patent documents are incorporated herein by referencein their entirety.

BACKGROUND

The present disclosure relates to schedule generation.

SUMMARY

Embodiments of the current disclosure provide for networked, autonomous,agglomerated resource utilization modelers. This disclosure alsoprovides for use cases thereof. Further embodiments of the currentdisclosure also provide for forecasting of general attributes, e.g., thegeneration of outputs in the form of a schedule. Yet further embodimentsof this disclosure may provide for the generation of general forecastsfor absenteeism, the number of employees required, and the like.

An example apparatus includes a scheduling factor interpretationcircuit, one or more agglomerate network circuits, one or more connectorcircuits, a schedule selector circuit, and a schedule provisioningcircuit. The scheduling factor interpretation circuit is structured tointerpret one or more scheduling factors. The one or more agglomeratenetwork circuits are each structured to generate a correspondingschedule. The one or more connector circuits are each structured to passat least one of the schedules as inputs to at least one of the one ormore agglomerate network circuits. The schedule selector circuit isstructured to select at least one of the schedules. The scheduleprovisioning circuit is structured to transmit the selected schedule.

Embodiments of the current disclosure provide for a method forpredicting schedules. The method includes: generating a first schedulevia a first agglomerate network; passing one or more portions of thefirst schedule to a second agglomerate network as input via a connector;and generating a second schedule via the second agglomerate networkbased at least in part on the one or more portions of the firstschedule. The method further includes transmitting the second schedule.In certain aspects, the method further includes weighing at least one ofthe first or the second agglomerate network to favor an employer over anemployee. In certain aspects the method further includes weighing atleast one of the first or the second agglomerate network to favor anemployee over an employer. In certain aspects, the method furtherincludes mixing at least one of the first schedule or the secondschedule with at least one other schedule.

Embodiments of the current disclosure provide for a system forpredicting schedules. The system includes a plurality of agglomeratenetworks, one or more connectors, a schedule selector circuit, and aschedule provisioning circuit. The plurality of agglomerate networks areeach structured to generate a corresponding schedule. The one or moreconnectors are each structured to pass at least one of the schedules asinput to at least one of the plurality of agglomerate networks. Theschedule selector circuit is structured to select at least one of theschedules. The schedule provisioning circuit is structured to transmitthe selected schedule.

Embodiment of the current disclosure provide for an apparatus thatincludes: a scheduling factor interpretation circuit, one or moreagglomerate network circuits, one or more connector circuits, a scheduleselector circuit, and a schedule provisioning circuit. The schedulingfactor interpretation circuit is structured to interpret one or morescheduling factors. The one or more agglomerate network circuits areeach structured to generate a corresponding schedule. The one or moreconnector circuits each structured to pass at least one of the schedulesas inputs to at least one of the one or more agglomerate networkcircuits. The schedule selector circuit is structured to select at leastone of the schedules. The schedule provisioning circuit is structured totransmit the selected schedule.

Embodiments of the current disclosure provide for a method forconfiguring a scheduling system. The method includes: generating aplurality of schedules for a plurality of targets using differentconfigurations of an agglomerate network; determining a performancescore of the plurality of schedules; and identifying configurations ofthe agglomerate network and targets with schedules above a performancescore above a threshold. The method further includes receiving a requestfor a schedule for a target; configuring the agglomerate network for thetarget based on the identified configurations; and generating theschedule using the configured agglomerate network. In certain aspects,tracking the performance includes tracking changes made to theschedules. In certain aspects, configuring the agglomerate networkincludes selecting scheduling models. In certain aspects, configuringthe agglomerate network includes selecting forecasting models. Incertain aspects, configuring the agglomerate network includesconfiguring data biases of data sources.

Embodiments of the current disclosure provide for an apparatus thatincludes a scenario interpretation circuit, a scenario analysis circuit,a data analysis circuit, a data source locator circuit, and a dataretrieval circuit. The scenario interpretation circuit is structured tointerpret schedule scenario data. The scenario analysis circuit isstructured to extract a scenario element from the schedule scenariodata. The data analysis circuit is structured to determine, based atleast in part on the extracted scenario element, a type of data forinclusion in the generation of schedule data corresponding to thescenario data. The data source locator circuit is structured to identifya source of the type of data for inclusion in the generation of theschedule data. The data retrieval circuit structured to retrieve datafrom the identified source. The data provisioning circuit structured totransmit the retrieved data.

Embodiments of the current disclosure provide for a method that includesinterpreting, via a scenario interpretation circuit, schedule scenariodata; and extracting, via a scenario analysis circuit, a scenarioelement from the schedule scenario data. The method further includesdetermining, via a data analysis circuit based at least in part on theextracted scenario element, a type of data for inclusion in thegeneration of schedule data corresponding to the scenario data; andidentifying, via a data source locator circuit, a source of the type ofdata for inclusion in the generation of the schedule data. The methodfurther includes retrieving, via a data retrieval circuit, data from theidentified source; and transmitting, via a data provisioning circuit,the retrieved data.

Embodiments of the current disclosure provide for a non-transitorycomputer-readable medium storing instructions. The stored instructionsadapt at least one processor to: interpret schedule scenario data;extract a scenario element from the schedule scenario data; anddetermine, based at least in part on the extracted scenario element, atype of data for inclusion in the generation of schedule datacorresponding to the scenario data. The stored instructions furtheradapt the at least one processor to: identify a source of the type ofdata for inclusion in the generation of the schedule data; retrieve datafrom the identified source; and transmit the retrieved data.

Embodiments of the current disclosure provide for an agglomerate networkfor generating schedule data. The agglomerate network includes aplurality of agglomerate network circuits, a connector circuit, and ahierarchical feature propagator (HFP). The plurality of agglomeratenetwork circuits is structured to generate schedule data correspondingto schedule scenario data. The connector circuit is structured to adjustat least one of an input of an agglomerate network circuit of theplurality or data outputted by the agglomerate network circuit. The HFPis structured to: interpret the schedule scenario data; extract ascenario element from the schedule scenario data; and determine, basedat least in part on the extracted scenario element, a type of data forinclusion in the generation of the schedule data. The HFP is furtherstructured to: identify a source of the type of data for inclusion inthe generation of the schedule data; retrieve data from the identifiedsource; and adjust the connector circuit to include the retrieved datain the generation of the schedule data.

Embodiments of the current disclosure may provide for an apparatus forincentive-based scheduling. The apparatus includes a scheduleinterpretation circuit, a shift analysis circuit, an incentivizercircuit, and an incentive provisioning circuit. The scheduleinterpretation circuit is structured to interpret schedule data. Theshift analysis circuit is structured to analyze the schedule data andidentify a shift. The incentivizer circuit is structured to determineincentive data for the shift. The incentive provisioning circuit isstructured to transmit the incentive data.

Embodiments of the current disclosure provide for a method forincentive-based scheduling. The method includes interpreting, via aschedule interpretation circuit, schedule data; and analyzing, via ashift analysis circuit, the schedule data. The method further includesidentifying, via the shift analysis circuit and based at least in parton the analysis of the schedule data, a shift; and determining, via anincentivizer circuit, incentive data for the shift; and transmitting,via an incentive provisioning circuit, the incentive data.

Embodiments of the current disclosure provide for another apparatus ofincentive-based scheduling. The apparatus includes a scheduleinterpretation circuit, a shift analysis circuit, an incentivizercircuit, and an incentive provisioning circuit. The scheduleinterpretation circuit is structured to interpret schedule data. Theshift analysis circuit is structured to: analyze the schedule data;identify a shift; and assign an employee value to the shift. Theincentivizer circuit is structured to determine incentive data for theshift based at least in part on the employee value. The incentiveprovisioning circuit is structured to transmit the incentive data.

Embodiments of the current disclosure provide for another method forincentive-based scheduling. The method includes: interpreting, via aschedule interpretation circuit, schedule data; analyzing, via a shiftanalysis circuit, the schedule data; and identifying, via the shiftanalysis circuit, a shift. The method further includes assigning, viathe shift analysis circuit, an employee value to the shift; determining,via an incentivizer circuit, incentive data for the shift based at leastin part on the employee value; and transmitting, via an incentiveprovisioning circuit, the incentive data.

Embodiments of the current disclosure provide for an agglomerate networkfor incentive-based scheduling. The agglomerate network includes ascheduler circuit, a connector circuit, and an incentivize analysiscircuit. The scheduler circuit is structured to output the scheduledata. The connector circuit is structured to adjust at least one of aninput to the scheduler circuit or the schedule data outputted by thescheduler circuit. The incentivize analysis circuit structured to:receive the schedule data via the connector circuit; identify a shift inthe schedule data; assign an employee value to the shift; determineincentive data for the shift based at least in part on the employeevalue; and transmit the incentive data.

Embodiments of the current disclosure provide for another apparatus forincentive-based scheduling. The apparatus includes a scheduleinterpretation circuit, a shift analysis circuit, an incentivizercircuit, and an incentive provisioning circuit. The scheduleinterpretation circuit is structured to interpret schedule data. Theshift analysis circuit structured to: analyze the schedule data,identify a portion of the schedule data, and assign an employee value tothe portion of the schedule data. The incentivizer circuit is structuredto determine incentive data for the portion of the schedule data basedat least in part on the employee value. The incentive provisioningcircuit is structured to transmit the incentive data.

Embodiments of the current disclosure provide for another method forincentive-based scheduling. The method includes: interpreting, viaschedule interpretation circuit, schedule data; analyzing, via a shiftanalysis circuit, the schedule data; identifying, via the shift analysiscircuit, a portion of the schedule data; and assigning, via the shiftanalysis circuit, an employee value to the portion of the schedule data.The method further includes determining, via an incentivizer circuit,incentive data for the portion of the schedule data based at least inpart on the employee value; and transmitting, via an incentiveprovisioning circuit, the incentive data.

Embodiments of the current disclosure provide for a non-transitorycomputer-readable medium storing instructions for incentive-basedscheduling. The stored instructions adapt at least one processor to:interpret schedule data; analyze the schedule data; and identify, basedat least in part on the analysis of the schedule data, a shift. Thestored instructions further adapt the at least one processor todetermine incentive data for the shift; and transmit the incentive data.

Embodiments of the current disclosure provide for an apparatus foremployee sharing/contracting. The apparatus includes a first scheduleinterpretation circuit, an availability determination circuit, a secondschedule interpretation circuit, a sharing circuit, and a sharedemployee provisioning circuit. The first schedule interpretation circuitis structured to interpret first schedule data for an employee of afirst entity. The availability determination circuit is structured todetermine availability data for the employee based at least in part onthe first schedule data. The second schedule interpretation circuit isstructured to interpret second schedule data corresponding to a secondentity. The sharing circuit is structured to determine, based at leastin part on the availability data and the second schedule data, that theemployee is available to work a shift corresponding to the secondentity. The shared employee provisioning circuit is structured totransmit an indication that the employee is available to work for theshift.

Embodiments of the current disclosure provide for a method for employeesharing/contracting. The method includes: interpreting, via a firstschedule interpretation circuit, first schedule data for an employee ofa first entity; determining, via an availability determination circuit,availability data for the employee based at least in part on the firstschedule data; and interpreting, via a second schedule interpretationcircuit, second schedule data corresponding to a second entity. Themethod further includes determining, via a sharing circuit and based atleast in part on the availability data and the second schedule data,that the employee is available to work a shift corresponding to thesecond entity; and transmitting, via a shared employee provisioningcircuit, an indication that the employee is available to work for theshift.

Embodiments of the current disclosure also provide for a non-transitorycomputer-readable medium storing instructions for employeesharing/contracting. The stored instructions adapt at least oneprocessor to: interpret first schedule data for an employee of a firstentity; determine availability data for the employee based at least inpart on the first schedule data; and interpret second schedule datacorresponding to a second entity. The stored instructions further adaptthe at least one processor to determine, based at least in part on theavailability data and the second schedule data, that the employee isavailable to work a shift corresponding to the second entity; andtransmit, an indication that the employee is available to work for theshift.

Embodiments of the current disclosure also provide for an agglomeratenetwork that provides employee sharing/contracting. The agglomeratenetwork includes a scheduler circuit, a connector circuit, and a sharedemployee contracting circuit. The scheduler circuit is structured tooutput first schedule data corresponding to a first entity. Theconnector circuit is structured to adjust at least one of an input tothe scheduler circuit or the first schedule data outputted by thescheduler circuit. The shared employee contracting circuit is structuredto: interpret the first schedule data; interpret second schedule datacorresponding to a second entity; and determine a need of the secondentity for a worker based at least in part on the second schedule data.The shared employee contracting circuit is further structured togenerate a change command value structured to trigger an adjustment tothe connector circuit to effect a change of at least one of the input tothe scheduler circuit or the first schedule data outputted by thescheduler circuit such that an employee is made available to fill theneed of the second entity for a worker. The shared employee contractingcircuit is further structured to transmit the change command value.

Embodiments include examples of networks, methods, and apparatus toenable schedule conformance. Embodiments may provide for scoring aschedule against company norms and altering the schedule to resolveconflicts.

In embodiments, an apparatus with a schedule interpretation circuit mayinterpret schedule data which is then used by a warden circuit todetermine, based at least in part on the schedule data, that a propertyof the schedule data violates a schedule norm, where the schedule normmay be based at least in part on historical schedule data. A correctiveaction circuit may generate, responsive to a determination that theproperty violates the schedule norm, a corrective action command valuestructured to trigger an adjustment to the schedule data, wherein theadjustment is structured to effect a change of the property such thatthe property retracts from violating the schedule norm. A correctiveaction provisioning circuit may then transmit the corrective actioncommand value. The schedule norm may be based at least in part onhistorical schedule data.

In embodiments, a method may include interpreting, via a scheduleinterpretation circuit, schedule data, and determining, via a wardencircuit and based at least in part on the schedule data, that a propertyof the schedule data violates a schedule norm. The method may furtherinclude generating, via a corrective action circuit responsive to thedetermination that the property violates the schedule norm, a correctiveaction command value. The corrective action command value may bestructured to trigger an adjustment to the schedule data, wherein theadjustment is structured to effect a change of the property such thatthe property retracts from violating the schedule norm. The method mayfurther include transmitting, via a corrective action provisioningcircuit, the corrective action command value. The schedule norm may bebased at least in part on historical schedule data.

In embodiments, an agglomerate network for generating schedule data mayinclude a schedule circuit to output schedule data and a connectorcircuit to adjust at least one of an input to the scheduler circuit orthe schedule data outputted by the scheduler circuit. A schedule wardencircuit may be structured to interpret the schedule data and determine,based at least in part on the schedule data, that a property of theschedule data violates a schedule norm. Responsive to the determinationthat the property violates the schedule norm, the schedule wardencircuit may generate a corrective action command value structured totrigger an adjustment to the connector circuit and transmit the correctcommand value. The adjustment to the connector circuit may include achange of at least one of the input to the scheduler circuit, or theschedule data outputted by the scheduler circuit, such that the propertyretracts from violating the schedule norm.

In embodiments, a non-transitory computer-readable medium may storeinstructions that adapt at least one processor to interpret scheduledata, and to determine, based at least in part on the schedule data,that a property of the schedule data violates a schedule norm. Theprocessor may be further adapted to generate, responsive to thedetermination that the property violates the schedule norm, a correctiveaction command value structured to trigger an adjustment to the scheduledata and transmit the corrective action command value. The adjustment isstructured to effect a change of the property such that the propertyretracts from violating the schedule norm. The schedule norm is based atleast in part on historical schedule data.

In embodiments, a method may include transmitting, via a local computingdevice, historical schedule data to a scheduling platform hosted on oneor more remote servers. The method may further include accessing, viathe local computing device, schedule data generated via the schedulingplatform, where the schedule data is based at least in part on aschedule warden circuit structured to conform the generated scheduledata to schedule norms determined from the historical schedule data. Themethod may further include executing a portion of a schedule that isbased at least in part on the schedule data.

In embodiments, an apparatus may include a schedule interpretationcircuit structured to interpret schedule data and a warden circuit. Thewarden circuit may be structured to: generate a plurality of scores forthe schedule data with respect to a plurality of schedule properties;retrieve a plurality of baseline values each corresponding to one of theschedule properties; and determine, based at least in part on theplurality of baseline values and the plurality of scores, that theschedule data is out of alignment with the baseline value for at leastone of the corresponding schedule properties. The apparatus may furtherinclude a corrective action circuit structured to generate, responsiveto the determination that the schedule data is out of alignment with thebaseline value for the at least one of the corresponding scheduleproperties, a corrective action command value structured to trigger anadjustment to the schedule data, wherein the adjustment is structured toeffect a change to the at least one schedule property that the scheduledata is out of alignment with. The apparatus may further include acorrective action provisioning circuit structured to transmit thecorrective action command value.

Embodiments of the current disclosure provide for methods and systemsfor proposing and executing scheduling experiments. The experiments maybe simulated and/or conducted in the real world with AI learning fromthe results. The selection of executed experiments, implementation ofchanges based on the results, and the like, may be automatic and/ormanual. Embodiments may provide for dials and/or sliders that providefor the introduction of how much risk (e.g., poor outcome) a user of thesystem can tolerate. Embodiments may provide for employees to opt-in toan experiment for an incentive, e.g., $1.00 more/hour, such as where theexperiment provides a more dynamic schedule, or provide for an employeeto opt-out of the experiment, such as to keep a more predictableschedule. Embodiments of schedule experimentation may be a module thatreceives inputs, e.g., a schedule and/or other data, e.g., biases, as:direct input, i.e., the schedule experimentation module may act as astandalone module; as direct input to an agglomerate network, e.g.,without use of connectors; and/or from connectors, e.g., the scheduleexperimentation module is one of a plurality of modules within anagglomerate network. Schedule experimentation may take the form of aschedule generation module within an agglomerate network that passes itsoutput (e.g., schedules) to other modules in the agglomerate network forevaluation where the other modules generate output(s), e.g., a bias. Theother modules may, in turn, feed the output back into the scheduleexperimentation module to form a feedback loop which tries to reachequilibrium and/or optimization of various biases in the agglomeratenetwork while keeping the generated schedules comparable to onesgenerated by managers. The connections between the scheduleexperimentation module and the various other modules of the agglomeratenetwork may be accomplished via connectors.

Embodiments of the current disclosure provide for an apparatus fortimekeeping and scheduling. The apparatus includes an employee surveyorcircuit, an embedding generator circuit, an artificial intelligencecircuit, a scheduling circuit, and a schedule provisioning circuit. Theemployee surveyor circuit interprets employee data. The embeddinggenerator circuit determines employee embeddings based at least in parton the employee data. Further, the artificial intelligence circuitgenerates a model based at least in part on the employee embeddings. Thescheduling circuit generates schedule data via the model, and theschedule provisioning circuit transmits the schedule data.

Embodiments of the current disclosure provide for a method fortimekeeping and scheduling. The method includes interpreting, via anemployee surveyor circuit, employee data. Further, the method includesdetermining employee embeddings based at least in part on the employeedata via an embedding generator circuit. The method also includesgenerating a model based at least in part on the employee embeddings viaan artificial intelligence circuit and generating, via a schedulingcircuit, schedule data via the model. The method can also includetransmitting, via a schedule provisioning circuit, the schedule data.

Embodiments of the current disclosure provide for another method fortimekeeping and scheduling. The method includes determining, using anembedding generator circuit, employee embeddings and generating a modelusing the employee embeddings via an artificial intelligence circuit.Further, the method includes generating, via a scheduling circuit, atimekeeping record using the model and the employee embeddings.

Embodiments of the current disclosure provide for another method fortimekeeping and scheduling. The method includes determining, using anembedding generator circuit, employee embeddings and generating a modelusing the employee embeddings via an artificial intelligence circuit.The method can also include generating, via a scheduling circuit, a listof recommended employees using the model and the employee embeddings.

Embodiments of the current disclosure provide for a non-transitorycomputer-readable medium storing instructions for timekeeping andscheduling. The stored instructions adapt at least one processor tointerpret, via an employee surveyor circuit, employee data anddetermine, via an embedding generator circuit, employee embeddings. Thestored instructions can also generate a model using the employeeembeddings via an artificial intelligence circuit. Further, the storedinstructions can generate, via a scheduling circuit, a schedule usingthe model and the employee embeddings.

Embodiments of the current disclosure provide for an apparatus forresponsive scheduling. The apparatus includes a schedule interpretationcircuit, a feedback interpretation circuit, a feedback influencercircuit, and a feedback influencer provisioning circuit. The scheduleinterpretation circuit is structured to interpret schedule data. Thefeedback interpretation circuit is structured to interpret feedback datacorresponding to the schedule data. The feedback influencer circuit isstructured to generate, based at least in part on the feedback data, afeedback influence command value structured to effect a change of aproperty of the schedule data. The feedback influencer provisioningcircuit is structured to transmit the feedback influence command value.

Embodiments of the current disclosure provide for a method forresponsive scheduling. The method includes: interpreting, via a scheduleinterpretation circuit, schedule data; interpreting, via a feedbackinterpretation circuit, feedback data corresponding to the scheduledata; and generating, via a feedback influencer circuit and based atleast in part on the feedback data, a feedback influence command valuestructured to effect a change of a property of the schedule data. Themethod further includes transmitting, via a feedback influencerprovisioning circuit the feedback influence command value.

Embodiments of the current disclosure provide for an agglomerate networkfor responsive scheduling. The agglomerate network includes a schedulercircuit, a connector circuit, and a responsive scheduler circuit. Thescheduler circuit is structured to output the schedule data. Theconnector circuit is structured to adjust at least one of an input tothe scheduler circuit or the schedule data outputted by the schedulercircuit. The responsive scheduler circuit structured to: interpret theschedule data; and generate, based at least in part on feedback data, afeedback influence command value structured to trigger an adjustment toa connector, wherein the adjustment is structured to effect a change ofat least one of the input to the scheduler circuit or the schedule dataoutputted by the scheduler circuit. The responsive scheduler circuit isfurther structured to transmit the feedback influence command value.

Embodiments of the current disclosure provide for a non-transitorycomputer-readable medium storing instructions for responsive scheduling.The stored instructions adapt at least one processor to: interpretschedule data; interpret feedback data corresponding to the scheduledata; and generate, based at least in part on the feedback data, afeedback influence command value structured to effect a change of aproperty of the schedule data. The stored instructions further adapt theat least one processor to transmit, the feedback influence commandvalue.

Embodiments of the current disclosure provide for another method forresponsive scheduling. The method includes: transmitting, via a localcomputing device, feedback data to a scheduling platform hosted on oneor more remote servers; accessing, via the local computing device,schedule data generated via the scheduling platform based at least inpart on a responsive scheduler circuit; and executing a schedule basedat least in part on the schedule data. The method further includesinfluencing, via the responsive scheduler circuit, the schedule databased at least in part on the feedback data.

Embodiments of the current disclosure provide for an apparatus forschedule mimicking. The apparatus includes a historic scheduleinterpretation circuit, a mimicking circuit, and a schedule dataprovisioning circuit. The historic schedule interpretation circuit isstructured to interpret historical schedule data corresponding to aschedule designed, in part, by an entity. The mimicking circuit isstructured to: extract a schedule trend from the historical scheduledata; identify a portion of the historical schedule data correspondingto the extracted schedule trend; and generate schedule data based atleast in part on the identified portion. The schedule data provisioningcircuit is structured to transmit the schedule data.

Embodiments of the current disclosure provide for a method for schedulemimicking. The method includes: interpreting, via a historic scheduleinterpretation circuit, historical schedule data corresponding to aschedule designed, in part, by an entity; extracting, via a mimickingcircuit, a schedule trend from the historical schedule data; andidentifying, via the mimicking circuit, a portion of the historicalschedule data corresponding to the extracted schedule trend. The methodfurther includes generating, via the mimicking circuit, schedule databased at least in part on the identified portion; and transmitting, viaa schedule data provisioning circuit, the schedule data.

Embodiments of the current disclosure provide for another apparatus forschedule mimicking. The apparatus includes a historic scheduleinterpretation circuit, a mimicking circuit, and a mimic commandprovisioning circuit. The historic schedule interpretation circuit isstructured to interpret historical schedule data corresponding to aschedule designed, in part, by an entity. The mimicking circuit isstructured to: extract a schedule trend from the historical scheduledata; identify a portion of the historical schedule data correspondingto the extracted schedule trend; and generate a mimic command valuebased at least in part on the identified portion, wherein the mimiccommand value is structured to trigger an adjustment to schedule datagenerated by a scheduler circuit. The mimic command provisioning circuitstructured to transmit the mimic command value.

Embodiments of the current disclosure provide for another method forschedule mimicking. The method includes: interpreting, via a historicschedule interpretation circuit, historical schedule data correspondingto a schedule designed, in part, by an entity; extracting, via amimicking circuit, a schedule trend from the historical schedule data;and identifying, via the mimicking circuit, a portion of the historicalschedule data corresponding to the extracted schedule trend. The methodfurther includes generating, via the mimicking circuit, a mimic commandvalue based at least in part on the identified portion, wherein themimic command value is structured to trigger an adjustment to scheduledata generated by a scheduler circuit; and transmitting, via a mimiccommand provisioning circuit, the mimic command value.

Embodiments of the current disclosure provide for an agglomerant networkthat generates schedule data based at least in part on schedulemimicking. The agglomerate network includes a scheduler circuit, aconnector circuit, and a schedule mimicker circuit. The schedulercircuit is structured to output the schedule data. The connector circuitis structured to adjust at least one of an input to the schedulercircuit or the schedule data outputted by the scheduler circuit. Theschedule mimicker circuit is structured to: interpret historicalschedule data; extract a schedule trend from the historical scheduledata; and identify a portion of the schedule data corresponding to theextracted schedule trend. The schedule mimicker circuit is furtherstructured to generate a mimic command value based at least in part onthe identified portion. The mimic command value is structured to triggeran adjustment to the connector circuit, and the adjustment is structuredto effect a change of at least one of the input to the scheduler circuitor the schedule data outputted by the scheduler circuit. The schedulemimicker circuit is further structured to transmit the mimic commandvalue.

Embodiments of the current disclosure provide for a non-transitorycomputer-readable medium that stored instructions for schedulemimicking. The instructions adapt at least one processor to: interprethistorical schedule data corresponding to a schedule designed, in part,by an entity; extract a schedule trend from the historical scheduledata; and identify a portion of the historical schedule datacorresponding to the extracted schedule trend. The stored instructionsfurther adapt the at least one processor to: generate schedule databased at least in part on the identified portion; and transmit theschedule data.

Embodiments of the current disclosure provide for an apparatus forbootstrap scheduling. The apparatus includes an employee datainterpretation circuit, a bootstrap circuit, and a schedule dataprovisioning circuit. The employee data interpretation circuit isstructured to interpret employee data corresponding to a first employee.The bootstrap circuit is structured to: match the first employee to asecond employee via querying one or more databases based at least inpart on the employee data; retrieve historical schedule data associatedwith the second employee via querying the one or more databases; extracta schedule trend from the historical schedule data; identify a portionof the historical schedule data corresponding to the extracted scheduletrend; and generate, based at least in part on the identified portion,schedule data corresponding to the first employee. The schedule dataprovisioning circuit is structured to transmit the schedule data.

Embodiments of the current disclosure provide for a method for bootstrapscheduling. The method includes: interpreting employee datacorresponding to a first employee; matching the first employee to asecond employee via querying one or more databases based at least inpart on the employee data; and retrieving historical schedule dataassociated with the second employee via querying the one or moredatabases. The method further includes extracting a schedule trend fromthe historical schedule data; identifying a portion of the historicalschedule data corresponding to the extracted schedule trend; andgenerating, based at least in part on the identified portion, scheduledata corresponding to the first employee. The method further includestransmitting the schedule data.

Embodiments of the current disclosure provide for another apparatus forbootstrap scheduling. The apparatus includes a position datainterpretation circuit, a bootstrap circuit, and a schedule dataprovisioning circuit. The position data interpretation circuit isstructured to interpret position data corresponding to a first position.The bootstrap circuit is structured to: match the first position to asecond position via querying one or more databases based at least inpart on the position data; retrieve historical schedule data associatedwith the second position via querying the one or more databases; extracta schedule trend from the historical schedule data; identify a portionof the historical schedule data corresponding to the extracted scheduletrend; and generate, based at least in part on the identified portion,schedule data corresponding to the first position. The schedule dataprovisioning circuit is structured to transmit the schedule data.

Embodiments of the current disclosure provide for another method forbootstrap scheduling. The method includes: interpreting position datacorresponding to a first position; matching the first position to asecond position via querying one or more databases based at least inpart on the position data; and retrieving historical schedule dataassociated with the second position via querying the one or moredatabases. The method further includes: extracting a schedule trend fromthe historical schedule data; identifying, a portion of the historicalschedule data corresponding to the extracted schedule trend; andgenerating, based at least in part on the identified portion, scheduledata corresponding to the first position. The method further includestransmitting the schedule data.

Embodiments of the current disclosure provide for a non-transitorycomputer-readable medium storing instructions for bootstrap scheduling.The stored instructions adapt at least one processor to: interpretemployee data corresponding to a first employee; match the firstemployee to a second employee via querying one or more databases basedat least in part on the employee data; and retrieve historical scheduledata associated with the second employee via querying the one or moredatabases. The stored instructions further adapt the at least oneprocessor to extract a schedule trend from the historical schedule data;identify a portion of the historical schedule data corresponding to theextracted schedule trend; and generate, based at least in part on theidentified portion, schedule data corresponding to the first employee.The stored instructions further adapt the at least one processor totransmit the schedule data.

Embodiments of the current disclosure provide for another non-transitorycomputer-readable medium storing instructions for bootstrap scheduling.The stored instructions adapt at least one processor to: interpretposition data corresponding to a first position; match the firstposition to a second position via querying one or more databases basedat least in part on the position data; and retrieve historical scheduledata associated with the second position via querying the one or moredatabases. The stored instructions further adapt the at least oneprocessor to extract a schedule trend from the historical schedule data;identify a portion of the historical schedule data corresponding to theextracted schedule trend; and generate, based at least in part on theidentified portion, schedule data corresponding to the first position.The stored instructions further adapt the at least one processor totransmit the schedule data.

Embodiments of the current disclosure provide for another apparatus forbootstrap scheduling. The apparatus includes an employee datainterpretation circuit, a bootstrap circuit, and a schedule dataprovisioning circuit. The employee data interpretation circuit isstructured to interpret first employee profile data corresponding to afirst employee. The bootstrap circuit is structured to: match the firstemployee profile data to second employee profile data via querying oneor more databases; retrieve historical schedule data associated with thesecond employee profile data via querying the one or more databases; andgenerate, based at least in part on the retrieved historical scheduledata, schedule data corresponding to the first employee. The scheduledata provisioning circuit is structured to transmit the schedule data.

Embodiments of the current disclosure provide for another method forbootstrap scheduling. The method includes: interpreting first employeeprofile data corresponding to a first employee; matching the firstemployee profile data to second employee profile data via querying oneor more databases; and retrieving historical schedule data associatedwith the second employee profile data via querying the one or moredatabases. The method further includes generating based at least in parton the retrieved historical schedule data, schedule data correspondingto the first employee. The method further includes transmitting theschedule data.

Embodiments of the current disclosure provide for another non-transitorycomputer-readable medium storing instructions for bootstrap scheduling.The stored instructions adapt at least one processor to: interpret firstemployee profile data corresponding to a first employee; match the firstemployee profile data to second employee profile data via querying oneor more databases; and retrieve historical schedule data associated withthe second employee profile data via querying the one or more databases.The stored instructions further adapt the at least one processor to:generate based at least in part on the retrieved historical scheduledata, schedule data corresponding to the first employee; and transmitthe schedule data.

Embodiments of the current disclosure provide for an apparatus forself-organizing an agglomerate network, the apparatus including ascenario interpretation circuit, a scenario analysis circuit, anarchitect circuit, and an architecture provisioning circuit. Thescenario interpretation circuit interprets schedule scenario data. Thescenario analysis circuit extracts one or more scenario elements fromthe schedule scenario data. The architect circuit: identifies, based atleast in part on the one or more scenario elements, one or moreagglomerate network circuits and one or more connector circuits; andgenerates agglomerate network architecture data that defines, in part, astructural relationship between at least one of the one or moreagglomerate network circuits and at least one of the one or moreconnector circuits. The architecture provisioning circuit transmits theagglomerate network architecture data.

Embodiments of the current disclosure provide for a method forself-organizing an agglomerate network is provided. The method includes:interpreting, via a scenario interpretation circuit, schedule scenariodata; extracting, via a scenario analysis circuit, one or more scenarioelements from the schedule scenario data; and identifying, via anarchitect circuit and based at least in part on the one or more scenarioelements, one or more agglomerate network circuits and one or moreconnector circuits. The method further includes generating, via thearchitect circuit, agglomerate network architecture data that defines,in part, a structural relationship between at least one of the one ormore agglomerate network circuits and at least one of the one or moreconnector circuits; and transmitting, via an architecture provisioningcircuit, the agglomerate network architecture data.

Embodiments of the current disclosure provide for another apparatus forself-organizing an agglomerate network, the apparatus including ascenario interpretation circuit, a scenario analysis circuit, anarchitect circuit, and an assembly circuit. The scenario interpretationcircuit interprets schedule scenario data. The scenario analysis circuitextracts one or more scenario elements from the schedule scenario data.The architect circuit: identifies, based at least in part on the one ormore scenario elements, one or more agglomerate network circuits and oneor more connector circuits; and generates agglomerate networkarchitecture data that defines, in part, one or more structuralrelationships between at least one of the one or more agglomeratenetwork circuits and at least one of the one or more connector circuits.The assembly circuit assembles the one or more agglomerate networkcircuits and the one or more connector circuits based at least in parton the one or more structural relationships.

Embodiments of the current disclosure provide for another method forself-organizing an agglomerate network is provided. The method includes:interpreting, via a scenario interpretation circuit, schedule scenariodata; extracting, via a scenario analysis circuit, one or more scenarioelements from the schedule scenario data; and identifying, via anarchitect circuit and based at least in part on the one or more scenarioelements, one or more agglomerate network circuits and one or moreconnector circuits. The method further includes generating, via thearchitect circuit, agglomerate network architecture data that defines,in part, one or more structural relationships between at least one ofthe one or more agglomerate network circuits and at least one of the oneor more connector circuits; and assemble, via an assembly circuit, theone or more agglomerate network circuits and the one or more connectorcircuits based at least in part on the one or more structuralrelationships.

Embodiments of the current disclosure provide for an apparatus forextended horizon scheduling. The apparatus includes a scheduleinterpretation circuit, an objective interpretation circuit, a horizonobjective analysis circuit, and a promotive action provisioning circuit.The schedule interpretation circuit interprets schedule data, theobjective interpretation circuit interprets objective data, and theschedule trend analysis circuit extracts a trend from the schedule data.The horizon objective analysis circuit: determines whether the extractedtrend furthers or impedes an objective defined, in part, by theobjective data, and responsive to a determination that the extractedtrend impedes the objective, generates a promotive action command valuestructured to trigger an adjustment to the schedule data. The adjustmentis structured to mitigate the extracted trend from impeding theobjective. The promotive action provisioning circuit transmits thepromotive action command value.

Embodiments of the current disclosure provide for a method for extendedhorizon scheduling that includes: interpreting, via a scheduleinterpretation circuit, schedule data; interpreting, via an objectiveinterpretation circuit, objective data; extracting, via a schedule trendanalysis circuit, a trend from the schedule data; determining, viahorizon objective analysis circuit, whether the extracted trend furthersor impedes an objective defined, in part, by the objective data; andresponsive to a determination that the extracted trend impedes theobjective, generating, via the horizon objective analysis circuit, apromotive action command value structured to trigger an adjustment tothe schedule data. The adjustment is structured to mitigate theextracted trend from impeding the objective. The method further includestransmitting, via a promotive action provisioning circuit, the promotiveaction command value.

Embodiments of the current disclosure provide for an agglomerate networkfor generating schedule data. The agglomerate network includes: ascheduler circuit, a connector circuit, and an extended horizonevaluation circuit. The scheduler circuit is structured to outputschedule data. The connector circuit is structured to adjust at leastone of an input to the scheduler circuit or the schedule data outputtedby the scheduler circuit. The extended horizon evaluation circuitstructured to: interpret the schedule data; interpret objective data;extract a trend from the schedule data; determine whether the extractedtrend furthers or impedes an objective defined, in part, by theobjective data; responsive to a determination that the extracted trendimpedes the objective, generate a promotive action command valuestructured to trigger an adjustment to the connector circuit to effect achange of at least one of the input to the scheduler circuit or theschedule data outputted by the scheduler circuit such that the extractedtrend is mitigated from impeding the objective; and transmit thepromotive command value.

Embodiments of the current disclosure provide for a non-transitorycomputer-readable medium storing instructions. The instructions adapt atleast one processor to: interpret schedule data; interpret objectivedata; extract a trend from the schedule data; and determine whether theextracted trend furthers or impedes an objective defined, in part, bythe objective data. The stored instructions further adapt the at leastone processor to: responsive to a determination that the extracted trendimpedes the objective, generate a promotive action command valuestructured to trigger an adjustment to the schedule data, wherein theadjustment is structured to mitigate the extracted trend from impedingthe objective; and transmit the promotive action command value.

Embodiments of the current disclosure provide for another apparatus forextended horizon scheduling. The apparatus includes a scheduleinterpretation circuit, an objective interpretation circuit, a scheduletrend analysis circuit, a horizon objective analysis circuit, and apromotive action provisioning circuit. The schedule interpretationcircuit is structured to interpret schedule data. The objectiveinterpretation circuit is structured to interpret objective data. Theschedule trend analysis circuit is structured to extract a trend fromthe schedule data. The horizon objective analysis circuit structured to:interpret a baseline score of the extracted trend, the baseline scorecorresponding to an objective defined, in part, by the objective data;score the extracted trend with respect to the objective; compare thescore to the baseline score to determine a distance between the scoreand the baseline score, and generate a promotive action command valuestructured to trigger an adjustment to the schedule data, wherein theadjustment is structured to adjust the schedule data to change thedistance. The promotive action provisioning circuit is structured totransmit the promotive action command value.

Embodiments of the current disclosure may provide for a method forextended horizon scheduling. The method includes: interpreting, via aschedule interpretation circuit, schedule data; interpreting, via anobjective interpretation circuit, objective data; extracting, via aschedule trend analysis circuit, a trend from the schedule data; andinterpreting, via a horizon objective analysis circuit, a baseline scoreof the extracted trend, the baseline score corresponding to an objectivedefined, in part, by the objective data. The method further includes:scoring, via the horizon objective analysis circuit, the extracted trendwith respect to the objective; comparing, via the horizon objectiveanalysis circuit, the score to the baseline score to determine adistance between the score and the baseline score; and generating, viathe horizon objective analysis circuit, a promotive action command valuestructured to trigger an adjustment to the schedule data, wherein theadjustment is structured to adjust the schedule data to change thedistance. The method further includes transmitting, via a promotiveaction provisioning circuit, the promotive action command value.

Embodiments of the current disclosure provide for a non-transitorycomputer-readable medium storing instructions. The stored instructionsadapt at least one processor to: interpret schedule data; interpretobjective data; extract a trend from the schedule data; and interpret abaseline score of the extracted trend, the baseline score correspondingto an objective defined, in part, by the objective data. The storedinstructions further adapt the at least one processor to score theextracted trend with respect to the objective; compare the score to thebaseline score to determine a distance between the score and thebaseline score; and generate a promotive action command value structuredto trigger an adjustment to the schedule data, wherein the adjustment isstructured to adjust the schedule data to change the distance. Thestored instructions further adapt the at least one processor to transmitthe promotive action command value.

The present disclosure also relates to devices and methods for adjustinga schedule responsive to a detected or predicted austere event toeliminate or otherwise mitigate the effect of the austere event on thebusiness operation related to the schedule.

In particular, embodiments of the current disclosure provide for anapparatus for scheduling responsive to an austere event. The apparatusincludes a schedule interpretation circuit structured to interpretschedule data. The apparatus further includes a mitigation circuitstructured to generate, based at least in part on the schedule data andaustere event data, a mitigation action command value structured totrigger an adjustment to the schedule data, wherein the adjustment isstructured to effect a change of a property of the schedule data tomitigate an effect of an austere event corresponding to the austereevent data on one or more entities associated with the schedule data.The apparatus further includes a mitigation action provisioning circuitstructured to transmit the mitigation action command value.

Embodiments of the current disclosure also provide for a method forscheduling responsive to an austere event. The method includes:interpreting, via a schedule interpretation circuit, schedule data;generating, via a mitigation circuit and based at least in part on theschedule data and austere event data, a mitigation action command valuestructured to trigger an adjustment to the schedule data. The adjustmentis structured to effect a change of a property of the schedule data tomitigate an effect of an austere event corresponding to the austereevent data on one or more entities associated with the schedule data.The method further includes transmitting, via a mitigation actionprovisioning circuit, the mitigation action command value.

Embodiments of the current disclosure further provide for an agglomeratenetwork that generates schedule data responsive to an austere event. Theagglomerate network includes: a scheduler circuit; a connector circuit;and an austere event circuit. The scheduler circuit is structured tooutput schedule data. The connector circuit is structured to adjust atleast one of an input to the scheduler circuit or the schedule dataoutputted by the scheduler circuit. The austere event circuit isstructured to: interpret the schedule data; and generate, based at leastin part on the schedule data and austere event data, a mitigation actioncommand value structured to trigger an adjustment to the connectorcircuit. The adjustment is structured to effect a change of at least oneof the input to the scheduler circuit or the schedule data outputted bythe scheduler circuit to mitigate an effect of an austere eventcorresponding to the austere event data on one or more entitiesassociated with the schedule data. The austere event circuit isstructured to transmit the mitigation action command value.

Embodiments of the current disclosure further provide for anon-transitory computer-readable medium that stores instructions forgenerating a schedule responsive to an austere event. The instructionsadapt at least one processor to interpret schedule data; and generate,based at least in part on the schedule data and austere event data, amitigation action command value structured to trigger an adjustment tothe schedule data. The adjustment is structured to effect a change of aproperty of the schedule data to mitigate an effect of an austere eventcorresponding to the austere event data on one or more entitiesassociated with the schedule data. The instructions further adapt the atleast one processor to transmit the mitigation action command value.

Embodiments of the current disclosure further provide for another methodfor adjusting a schedule responsive to an austere event. The methodincludes: transmitting, via a local computing device, austere event datato a scheduling platform hosted on one or more remote servers; andaccessing, via the local computing device, schedule data generated viathe scheduling platform based at least in part on an austere eventcircuit. The method further includes executing a schedule based at leastin part on the schedule data. The schedule is structured to mitigate aneffect of an austere event corresponding to the austere event data onone or more entities associated with the schedule data.

Embodiments of the current disclosure provide for a bidding and/or othermarket-based process for letting workers compete for shifts and fordetermining insights from market activity, e.g., most favorite shifts,least favorite shifts, preferred shift patterns, etc. Workers may beallotted a currency to bid on and/or “purchase” shifts, and the insightsmay be used to adjust one or more modules in an agglomerate network. Aworker may be an employee, a contractor, a free-lance employee, atemporary employee (such as a travel nurse), and the like. Embodimentsmay include a schedule warden to detect unfair trade groups, e.g., acircle of friends who only trade among themselves to the advantage ofthe group and the detriment of others. Embodiments of the marketplacemay be used to determine what is a “good” or a “bad” schedule, i.e.,“let the market decide.” The “good” or “bad” schedules may be used todetermine what is a “fair” schedule? Embodiments may seek to balance thebenefits of a worker being made available simultaneously in multipleorganizations with appropriate privacy controls. In embodiments, shifts(if reoccurring) may be rated by the workers. In embodiments, workersmay have properties (viewable to an AI or a manager), where certainshifts are made available to workers based on their properties. Inembodiments, different incentives for the same shift may be offered todifferent workers. In embodiments, differences in offered incentives maybe based on worker properties. For example, a high-level or more seniorworker may be offered a better incentive than a lower ranked, newerworker. Embodiments may provide for other manners of limiting shifts toparticular groups of workers. In embodiments, an AI or a manager mayprovide feedback regarding a worker's performance of a task, e.g.,timeliness, accuracy, etc., which may, in turn, affect the worker'srating. Embodiments of the current disclosure may also provide forworkers to sell and/or trade shifts. In embodiments, shifts may only besold and/or traded to workers who meet the rating and/or other criteriarequired by the shift, e.g., a task may only be traded to a worker whohas the same or higher rating than the currently assigned employee.

Embodiments of the marketplace may be hosted on a server, internaland/or external to a corporation and accessible via remote computingdevices, e.g., phones, tables, workstations, etc. One advantage ofcomputerizing the marketplace is the ability to make such a systempractical to use by an organization with a large number of tasks and/orworkers, e.g., a highly responsive system that won't take longer tohandle the offer/bid/acceptance matching process than the actual shiftitself.

An example apparatus includes an agglomerate network circuit structuredto interpret input data and transmit output data, and a connectorcircuit structured to bias at least one of the input data prior tointerpretation by the agglomerate network circuit or the output dataprior to transmission by the agglomerate network circuit.

An example procedure includes operations for interpreting, via anagglomerate network circuit, input data, operations for transmitting,via the agglomerate network circuit, output data, and operations forbiasing, via a connector circuit, at least one of the input data priorto interpretation via the agglomerate network circuit or the output dataprior to transmission via the agglomerate network circuit.

An example apparatus includes a plurality of agglomerate networkcircuits each structured to interpret input data and transmit outputdata, and a plurality of connector circuits each structured to:interpret the output data of a first corresponding agglomerate networkcircuit of the plurality, bias the interpreted output data, and transmitthe biased interpreted output data as the input data of a secondcorresponding agglomerate network circuit of the plurality.

An example procedure includes operations of interpreting, via a firstagglomerate network circuit, first input data, operations of generating,via the first agglomerate network circuit, first output data based atleast in part on the first input data, operations of biasing, via afirst connector circuit, the first output data, operations ofinterpreting, via a second agglomerate network circuit, the biased firstoutput data as second input data, operations of generating, via thesecond agglomerate network circuit, second output data based at least inpart on the second input data, operations of biasing, via a secondconnector circuit, the second output data, operations of interpreting,via a third agglomerate network circuit, the biased second output dataas third input data, operations of generating, via the third agglomeratenetwork circuit, third output data based at least in part on the thirdinput data, and operations of transmitting the third output data.

An example non-transitory computer-readable medium includes storedinstructions that adapt at least one processor to: interpret, via afirst agglomerate network circuit, first input data, generate, via thefirst agglomerate network circuit, first output data based at least inpart on the first input data, bias, via a first connector circuit, thefirst output data, interpret, via a second agglomerate network circuit,the biased first output data as second input data, generate, via thesecond agglomerate network circuit, second output data based at least inpart on the second input data, bias, via a second connector circuit, thesecond output data, interpret, via a third agglomerate network circuit,the biased second output data as third input data, generate, via thethird agglomerate network circuit, third output data based at least inpart on the third input data, and transmit the third output data.

An example apparatus includes an agglomerate network circuit structuredto interpret input data and transmit output data, and a connectorcircuit structured to: receive biasing parameters for the input data,categorize the input data based on types of biases that can be appliedto the input data, map biasing parameters to the categorized input data,and bias the input data based on the mapping.

An example apparatus includes an agglomerate network circuit structuredto receive input data, a bias interpretation circuit structured toreceive biasing parameters for the input data, a categorizing circuitstructured to categorize the input data based on types of biases thatcan be applied, a mapping circuit structured to map the biasingparameters to the categorized input data, and a biasing circuitstructured to bias at least one of the input data based on the mapping,wherein the agglomerate network circuit is further structured totransmit the biased input data.

An example apparatus includes an agglomerate network circuit structuredto receive input data, a bias interpretation circuit structured toreceive biasing parameters for the input data, a mapping circuitstructured to map the biasing parameters to the input data, a biasingmethodology circuit structured to determine a biasing method based onthe mapping, and a biasing circuit structured to bias, using thedetermined biasing method, the input data based on the mapping, whereinthe agglomerate network circuit is further structured to transmit thebiased input data.

An example apparatus includes an agglomerate network circuit structuredto receive input data, perform manipulations on the data and generateoutput data, a bias interpretation circuit structured to receive biasingparameters for the output data, a mapping circuit structured to modifyinput data and identify correlations between input modifications andchanges to the output data, and a biasing circuit structured toiteratively trigger different input data modifications at the mappingcircuit and identify input data modifications that result in desiredbiasing parameters for the output data.

An example procedure includes operations of receiving, via anagglomerate network circuit, input data, receiving biasing parametersfor the input data, categorizing the input data based on types of biasesthat can be applied, mapping biasing parameters to the categorized inputdata, biasing, at least one of the input data based on the mapping, andtransmitting, via the agglomerate network circuit, the biased inputdata.

An example procedure includes operations of receiving, via anagglomerate network circuit, input data, receiving biasing parametersfor the input data, mapping biasing parameters to the input data,determining biasing methods based on the mapping, biasing, using thedetermined biasing methods, the input data based on the mapping, andtransmitting, via the agglomerate network circuit, the biased inputdata.

An example non-transitory computer-readable medium stores instructionsthat adapt at least one processor to receive input data, receive biasingparameters for the input data, categorize the input data based on typesof biases that can be applied, map the biasing parameters to thecategorized input data, bias the input data based on the mapping, andtransmit the biased input data.

An example non-transitory computer-readable medium stores instructionsthat adapt at least one processor to receive input data, receive biasingparameters for the input data, map the biasing parameters to the inputdata, determine a biasing method based on the mapping, bias, using thedetermined biasing method, the input data based on the mapping, andtransmit the biased input data.

An example agglomerate network for generating schedule data includes ascheduler circuit structured to output a first schedule data, a firstmodule structured to apply a first bias to the first schedule data andgenerate second schedule data, a second module structured to receive thesecond schedule data from the first module and structured to manipulatethe second schedule data and generate third schedule data, where themanipulation includes applying a second bias to the third schedule data,a bias monitoring module structured to monitor the first bias and thesecond bias and identify conflicting elements of the first and secondbiases, and a connector circuit structured to adjust, responsive toidentifying conflicting bias parameters of at least one of the schedulercircuit, the first module, or the second module to resolve theconflicting biases.

An example apparatus includes a first module configured to apply a firstbias to first data to generate second data, a second module configuredto receive the second data and configured to apply a second bias to thesecond data, a first bias monitoring module configured to calculate acombined bias score based at least in part on the first bias and thesecond bias, a bias adjustment circuit configured to determine that thecombined bias score is above a threshold value and adjust the secondbias, and a bias notification circuit structured to transmit anindication that the combined bias score was determined to be above thethreshold value.

An example procedure includes operations for propagating data from afirst module to a second module, propagating an indication of a firstbias applied to the data from the first module to the second module,applying a second bias at the second module, computing a combined biasscore based on the first bias and the second bias, determining that thecombined bias score is above a bias threshold value, and transmitting anindication to the first module of the bias threshold value.

An example procedure includes operations for propagating data from afirst module to a second module, applying a first bias to the data atthe first module, applying a second bias to the data at the secondmodule, monitoring a combined bias that is based at least in part on thefirst bias and the second bias, determining that the combined bias isless than the first bias or the second bias; and adjusting at least oneof the first bias or the second bias to reduce a magnitude of thecombined bias.

In embodiments, a schedule spreader may be used to make and/or recommendchanges to a schedule based on beneficial changes to a schedule ofanother department within an organization and/or across organizations.

Accordingly, embodiments of the current disclosure provide for anapparatus for schedule spreading. The apparatus includes a scheduleinterpretation circuit, a schedule adjustment circuit, a spread commandcircuit, and a spread command provisioning circuit. The scheduleinterpretation circuit is structured to interpret schedule data.Further, the schedule adjustment circuit is structured to maintain alist of recommended schedule adjustments. Each recommended scheduleadjustment corresponds to one of a plurality of schedule parameters. Theschedule parameters of the list of recommended schedule adjustments arereferred to as the first schedule parameters. It will be understood thatthe list of recommended schedule adjustments may include one or moreschedule parameters. The schedule adjustment circuit analyzes theschedule data and identifies a schedule parameter referred to as asecond schedule parameter. Further, the schedule adjustment circuitidentifies a recommended schedule adjustment from the list ofrecommended schedule adjustments. The identification is performed bymatching the second schedule parameter to one of the plurality of thefirst schedule parameters. The one of the plurality of first scheduleparameters corresponds to the recommended schedule adjustment. Thespread command circuit is structured to generate a spread command valuestructured to trigger a change in the schedule data. The spread commandcircuit operates in response to the identified recommended scheduleadjustment performed by the schedule adjustment circuit. The change inthe schedule data includes adjusting the schedule data according to therecommended schedule adjustment. The spread command provisioning circuitis structured to transmit the spread command value.

Embodiments of the current disclosure also provide for a method forschedule spreading. The method includes interpreting, via a scheduleinterpretation circuit, schedule data. The method also includesmaintaining a list of recommended schedule adjustments via a scheduleadjustment circuit. Each recommended schedule adjustment corresponds toone of a plurality of first schedule parameters. Further, the methodincludes analyzing, via the schedule adjustment circuit, the scheduledata to identify a second schedule parameter. The method includesidentifying, via the schedule adjustment circuit, a recommended scheduleadjustment from the list via matching the second schedule parameter toone of the plurality of first schedule parameters corresponding to therecommended schedule adjustment. The method includes generating, via aspread command circuit, responsive to the identified recommendedschedule adjustment, a spread command value structured to trigger achange to the schedule data. The change includes adjusting the scheduledata according to the recommended schedule adjustment. The methodincludes transmitting, via a spread command provisioning circuit, thespread command value.

Another embodiment is an agglomerate network. The agglomerate networkfor generating schedule data includes a scheduler circuit structured tooutput the schedule data; a connector circuit structured to adjust atleast one of input to the scheduler circuit or the schedule dataoutputted by the scheduler circuit. The agglomerate network alsoincludes a schedule spreader circuit structured to maintain a list ofrecommended schedule adjustments. Each recommended schedule adjustmentcorresponds to one of a plurality of first schedule parameters. Further,the agglomerate network may provide for analyzing the schedule data toidentify a second schedule. The agglomerate network may also provide foridentifying a recommended schedule adjustment from the list via matchingthe second schedule parameter to one of the plurality of first scheduleparameters corresponding to the recommended schedule adjustment.Responsive to the identified recommended schedule adjustment, theagglomerate network may further provide for generating a spread commandvalue structured to trigger a change to the schedule data. The change tothe schedule data includes adjusting the connector circuit according tothe recommended schedule adjustment. Further, the agglomerate networkmay provide for transmitting of the spread command value.

Embodiments of the current disclosure provide for an apparatus thatincludes a plurality of agglomerate network circuits and a plurality ofconnector circuits. The plurality of agglomerate network circuits areeach structured to interpret input data and transmit output data. Theplurality of connector circuits are each structured to: interpret theoutput data of a corresponding agglomerate network circuit of theplurality, and execute a connector action based at least in part on theinterpreted output data. The connector action performed by at least oneof the connector circuits of the plurality at least one of: propagatesthe output data of a first agglomerate network circuit of the pluralityas the input data of a second agglomerate network circuit of theplurality, biases the output data of an agglomerate network circuit ofthe plurality, realigns the output data of an agglomerate networkcircuit of the plurality, weights the outputs of at least twoagglomerate network circuits of the plurality, or propagates aconfidence value, corresponding to the output data generated by a firstagglomerate network circuit of the plurality, to a second agglomeratenetwork circuit of the plurality with the corresponding output data.

Embodiments of the current disclosure provide for a method that includesgenerating schedule data via a plurality of agglomerate network circuitseach structured to: interpret input data, and generate output data basedat least in part in the interpreted input data. The method furtherincludes executing a plurality of connector actions via one or moreconnector circuits. The plurality of connector actions effect generationof the schedule data and include at least one of: propagating the outputdata of a first agglomerate network circuit of the plurality as theinput data of a second agglomerate network circuits of the plurality,biasing the output data of an agglomerate network circuit of theplurality, realigning the output data of an agglomerate network circuitof the plurality to be within an acceptable range, weighting the outputsof at least two agglomerate network circuits of the plurality, orpropagating a confidence value, corresponding to the output datagenerated by a first agglomerate network circuit of the plurality, to asecond agglomerate network circuit of the plurality with thecorresponding output data. The method further includes transmitting theschedule data.

Embodiments of the current disclosure provide for a non-transitorycomputer-readable medium storing instructions. The stored instructionsadapt at least one processor to: generate schedule data via a pluralityof models each structured to: interpret input data and generate outputdata based at least in part on the interpreted input data. The storedinstructions further adapt the at least one processor to execute aplurality of connector actions via one or more connector circuits. Atleast one of the connector actions of the plurality at least one of:propagates the output data of a model of the plurality as the input dataof a second model of the plurality, biases the output data of a model ofthe plurality, realigns the output data of a model of the plurality tobe within an acceptable range, weights the outputs of at least twomodels of the plurality, or propagates a confidence value, correspondingto the output data generated by a first model of the plurality, to asecond model of the plurality with the corresponding output data. Thestored instructions further adapt the at least one processor to transmitthe schedule data.

Embodiments of the current disclosure provide for an apparatus thatincludes a plurality of agglomerate network circuits and a plurality ofconnector circuits. The plurality of agglomerate network circuits isstructured to generate schedule data. The plurality of connectorcircuits is structured to propagate data between each of the pluralityof agglomerate network circuits. The plurality of agglomerate networkcircuits includes a scheduler. At least one of the plurality ofconnector circuits is structured to adjust at least one of input data toat least one of the plurality of agglomerate network circuits or outputdata from the at least one of the plurality of agglomerate networkcircuits.

Embodiments of the current disclosure provide for apparatuses, methods,and networks for adjusting an architecture of an agglomerate schedulingnetwork by determining when a new connection, structure, data, bias, andthe like should be introduced into the network. Embodiments may usehistoric data, e.g., the inputs for a given scheduling scenario, theconfiguration of the agglomerate scheduling network used, the precitedresults, and/or actual results to generate an agglomerate network model.In embodiments, experiments may be run on the model to see if proposedchanges to the network might result in improved performance metricsprior to deployment of any changes to the network.

In embodiments, outputs of modules correlated to anomalies in schedulingmay be identified, and a determination may be made regarding whether thenetwork would benefit from the introduction of a module. If yes, wherethe module should be included, another determination may be made as tohow the module would affect the biases of other connectors. Examplemetrics to detect outputs that correlate to anomalies in scheduling mayinclude: a percentage of employee goals achieved under an assignedmanager, an amount of sales, an attendance rate, a turnover rate, andthe like. Different configurations for a new network may be tested todetermine what configuration may improve results.

An embodiment of an apparatus may include a historic data processingcircuit to interpret historic schedule performance data includinghistoric schedule data and performance metrics corresponding to thehistoric schedule data. The example apparatus may further include anetwork architecture processing circuit to interpret networkarchitecture data that describes properties of an agglomerate networkthat generates schedule data. A resolution analysis circuit may generatean adjustment command value partially based on the interpreted historicschedule performance data and the interpreted network architecture data.An adjustment provisioning circuit may transmit the adjustment commandvalue, where the adjustment command value to adjust the agglomeratenetwork in order to improve performance metric of the agglomeratenetwork by effecting an adjustment to the agglomerate network.

An embodiment of an agglomerate network for generating schedule data mayinclude a plurality of agglomerate network circuits to generate scheduledata and a connector circuit to propagate data between at least two ofthe plurality of agglomerate network circuits. The agglomerate networkmay further include a resolution determiner circuit to interprethistorical performance data and network architecture data and generate,based at least in part on the historical performance data and thenetwork architecture data, an adjustment command value structured toaffect an adjustment to the agglomerate network to improve a performancemetric. The resolution determiner circuit may further transmit theadjustment command value.

An embodiment of method for generating schedule data may includeinterpreting historic schedule performance data, wherein the historicschedule performance data includes historic schedule data andcorresponding performance metrics. The method may further includeinterpreting current network architecture data, wherein the currentnetwork architecture data includes a property of an agglomerate network.The method may further include generating, based at least in part on thehistoric schedule performance data and the network architecture data, anadjustment command value structured to affect an adjustment to thecurrent agglomerate network to improve a performance metric of thecurrent agglomerate network, and transmitting the adjustment commandvalue.

An embodiment of a non-transitory computer-readable medium, as disclosedherein, may store instructions that adapt at least one processor tointerpret historic schedule performance data, wherein the historicschedule performance data includes historic schedule data and a historicperformance metric corresponding to the historic schedule data, andinterpret network architecture data, wherein the network architecturedata includes a property of an agglomerate network. The processor may befurther adapted to generate, based at least in part on the historicschedule performance data and the network architecture data, anadjustment command value structured to affect an adjustment to theagglomerate network to improve a performance metric of the agglomeratenetwork, and transmit the adjustment command value.

In embodiments, scheduling modules may each generate a plurality ofinitial schedules. The schedules may then be evaluated and/or propagatedin an agglomerate network to determine which is the best schedule or todetermine a schedule that meets desired criteria. The number and/or typeof schedules generated by each module may be fixed and/or may bedynamically or algorithmically determined. In some embodiments, themodules may be configured to generate schedules that provide a varietyof different schedules or a top number of schedules. In embodiments, thescheduling modules may be configured to generate a threshold number ofschedules based on previous selections of schedules. The thresholdnumber may be determined by first generating a first number ofschedules, for example, ten (10) schedules, and then identifying whichof the first number of schedules is selected by the agglomerate network.If the selected schedule were at a first threshold number of thegenerated schedules, for example, if the selected schedule were theninth or tenth generated schedule, e.g., out of the ten schedules, thethreshold number of schedules may be increased to a second thresholdnumber of generated schedules, for example, fifteen (15) schedules. Ifthe selected schedule were the first of the second threshold number ofgenerated schedules, the threshold number of schedules may be decreased,for example, to five (5) schedules. The process of adjusting thethreshold number of generated schedules may be periodically orcontinuously adjusted to reduce unnecessary computations, while stillproviding enough schedules to find a suitable schedule.

Certain further aspects of the example apparatus are described herein,any one or more of which may be present in certain embodiments.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 depicts a platform for generating schedules using an agglomeratenetwork, in accordance with embodiments of the current disclosure;

FIG. 2 depicts an agglomerate network, in accordance with embodiments ofthe current disclosure;

FIG. 3 depicts an architecture for an autonomous agglomerated resourceutilization modeler, in accordance with embodiments of the currentdisclosure;

FIG. 4 depicts primary and secondary resource models in accordance withembodiments of the current disclosure;

FIG. 5 is a schematic diagram of an agglomerate network and ahierarchical feature propagator (HFP), in accordance with embodiments ofthe current disclosure;

FIG. 6 is a schematic diagram of an apparatus having one or more aspectsof an HFP, in accordance with embodiments of the current disclosure;

FIG. 7 is a method for an HFP, in accordance with embodiments of thecurrent disclosure;

FIG. 8 is a schematic diagram depicting an apparatus for anincentive-based scheduler, in accordance with an embodiment of thepresent disclosure;

FIG. 9 is a schematic diagram depicting certain further aspects of anapparatus for an incentive-based scheduler, in accordance with anembodiment of the present disclosure;

FIG. 10 is a schematic diagram depicting certain further aspects of anapparatus for an incentive-based scheduler, in accordance with anembodiment of the present disclosure;

FIG. 11 is a schematic diagram depicting certain further aspects of anapparatus for an incentive-based scheduler, in accordance with anembodiment of the present disclosure;

FIG. 12 is a flowchart depicting a method for connector equilibrium, inaccordance with an embodiment of the present disclosure;

FIG. 13 is a flowchart depicting certain further aspects of a method forconnector equilibrium, in accordance with an embodiment of the presentdisclosure;

FIG. 14 is a flowchart depicting certain further aspects of a method forconnector equilibrium, in accordance with an embodiment of the presentdisclosure;

FIG. 15 is a schematic diagram depicting an apparatus for anincentive-based scheduler, in accordance with an embodiment of thepresent disclosure;

FIG. 16 is a schematic diagram depicting certain further aspects of anapparatus for an incentive-based scheduler, in accordance with anembodiment of the present disclosure;

FIG. 17 is a flowchart depicting a method for connector equilibrium, inaccordance with an embodiment of the present disclosure;

FIG. 18 is a flowchart depicting certain further aspects of a method forconnector equilibrium, in accordance with an embodiment of the presentdisclosure;

FIG. 19 is a schematic diagram depicting an agglomerate network forgenerating schedule data, in accordance with an embodiment of thepresent disclosure;

FIG. 20 is a schematic diagram depicting certain further aspects of anagglomerate network for generating schedule data, in accordance with anembodiment of the present disclosure;

FIG. 21 is a schematic diagram depicting an apparatus for anincentive-based scheduler, in accordance with an embodiment of thepresent disclosure;

FIG. 22 is a schematic diagram depicting certain further aspects of anapparatus for an incentive-based scheduler, in accordance with anembodiment of the present disclosure;

FIG. 23 is a flowchart depicting a method for connector equilibrium, inaccordance with an embodiment of the present disclosure;

FIG. 24 is a flowchart depicting certain further aspects of a method forconnector equilibrium, in accordance with an embodiment of the presentdisclosure;

FIG. 25 is a block diagram depicting a non-transitory computer-readablemedium for connector equilibrium, in accordance with an embodiment ofthe present disclosure;

FIG. 26 is a block diagram depicting certain further aspects of anon-transitory computer-readable medium for connector equilibrium, inaccordance with an embodiment of the present disclosure;

FIG. 27 is a schematic diagram depicting an apparatus for employeesharing/contracting, in accordance with an embodiment of the currentdisclosure;

FIG. 28 is a schematic diagram depicting certain further aspects of anapparatus for employee sharing/contracting, in accordance with anembodiment of the current disclosure;

FIG. 29 is a flowchart depicting a method for employeesharing/contracting, in accordance with an embodiment of the currentdisclosure;

FIG. 30 is a flowchart depicting certain further aspects of a method foremployee sharing/contracting, in accordance with an embodiment of thecurrent disclosure;

FIG. 31 is a block diagram depicting a non-transitory computer-readablemedium for employee sharing/contracting, in accordance with anembodiment of the current disclosure;

FIG. 32 is a block diagram depicting certain further aspects of anon-transitory computer-readable medium for employeesharing/contracting, in accordance with an embodiment of the currentdisclosure;

FIG. 33 is a schematic diagram depicting an agglomerate network foremployee sharing/contracting, in accordance with an embodiment of thecurrent disclosure;

FIG. 34 is a schematic diagram depicting certain further aspects of anagglomerate network for employee sharing/contracting, in accordance withan embodiment of the current disclosure;

FIG. 35 depicts an evolution process flow, in accordance withembodiments of the current disclosure;

FIG. 36 depicts an agglomerate mode information propagation process, inaccordance with embodiments of the current disclosure;

FIG. 37 depicts an apparatus for determining when scheduling conditionsfall outside of an entity's normal scheduling practice, in accordancewith embodiments of the current disclosure;

FIG. 38 depicts schedule properties, in accordance with embodiments ofthe current disclosure;

FIG. 39 depicts an embodiment of a warden circuit, in accordance withembodiments of the current disclosure;

FIG. 40 depicts a method for determining when schedule conditions falloutside of an entity's normal scheduling practice; in accordance withembodiments of the current disclosure;

FIG. 41 depicts a method for adjusting a schedule to assure equitableschedules, in accordance with embodiments of the current disclosure;

FIG. 42 depicts an agglomerate network for generating schedule data; inaccordance with embodiments of the current disclosure;

FIG. 43 depicts an apparatus for adjusting schedules, in accordance withembodiments of the current disclosure;

FIG. 44 is a flow diagram depicting a method for executing schedulingexperiments, in accordance with an embodiment of the current disclosure;

FIG. 45 is a flow diagram depicting certain further aspects of a methodfor executing scheduling experiments, in accordance with an embodimentof the current disclosure;

FIG. 46 is a block diagram depicting an apparatus for executingscheduling experiments, in accordance with an embodiment of the currentdisclosure;

FIG. 47 is a block diagram depicting certain further aspects of anapparatus for executing scheduling experiments, in accordance with anembodiment of the current disclosure;

FIG. 48 is a block diagram depicting an agglomerate network forexecuting scheduling experiments, in accordance with an embodiment ofthe current disclosure;

FIG. 49 is a block diagram depicting certain further aspects of anagglomerate network for executing scheduling experiments, in accordancewith an embodiment of the current disclosure;

FIG. 50 depicts a method for schedule experimentation, in accordancewith embodiments of the current disclosure;

FIG. 51 is a schematic diagram of an apparatus for timekeeping andscheduling, in accordance with embodiments of the current disclosure;

FIG. 52 is another schematic diagram of another apparatus fortimekeeping and scheduling, in accordance with embodiments of thecurrent disclosure;

FIG. 53 is a flowchart depicting a method for timekeeping andscheduling, in accordance with embodiments of the current disclosure;

FIG. 54 is another flowchart of the method of FIG. 53 , in accordancewith embodiments of the current disclosure;

FIG. 55 is a flowchart depicting a method for timekeeping andscheduling, in accordance with embodiments of the current disclosure;

FIG. 56 is another flowchart depicting the method of FIG. 55 , inaccordance with embodiments of the current disclosure;

FIG. 57 is a flowchart depicting a method for timekeeping andscheduling, in accordance with embodiments of the current disclosure;

FIG. 58 is another flowchart depicting the method of FIG. 57 , inaccordance with embodiments of the current disclosure;

FIG. 59 is a schematic diagram depicting an apparatus for responsivescheduling, in accordance with an embodiment of the current disclosure;

FIG. 60 is a schematic diagram depicting certain further aspects of anapparatus for responsive scheduling, in accordance with an embodiment ofthe current disclosure;

FIG. 61 is a flowchart depicting a method for responsive scheduling, inaccordance with an embodiment of the current disclosure;

FIG. 62 is a flowchart depicting certain further aspects of a method forresponsive scheduling, in accordance with an embodiment of the currentdisclosure;

FIG. 63 is a schematic diagram depicting an agglomerate network forresponsive scheduling, in accordance with an embodiment of the currentdisclosure;

FIG. 64 is a schematic diagram depicting certain further aspects of anagglomerate network for responsive scheduling, in accordance with anembodiment of the current disclosure;

FIG. 65 is a block diagram depicting a non-transitory computer-readablemedium for responsive scheduling, in accordance with an embodiment ofthe current disclosure;

FIG. 66 is a block diagram depicting certain further aspects of anon-transitory computer-readable medium for responsive scheduling, inaccordance with an embodiment of the current disclosure;

FIG. 67 is a flowchart depicting a method for responsive scheduling, inaccordance with an embodiment of the current disclosure;

FIG. 68 is a flowchart depicting certain further aspects of a method forresponsive scheduling, in accordance with an embodiment of the currentdisclosure;

FIG. 69 is a schematic diagram depicting an apparatus for schedulemimicking, in accordance with embodiments of the current disclosure;

FIG. 70 is a schematic diagram depicting certain further aspects of anapparatus for schedule mimicking, in accordance with embodiments of thecurrent disclosure;

FIG. 71 is a flowchart depicting a method for schedule mimicking, inaccordance with embodiments of the current disclosure;

FIG. 72 is a flowchart depicting certain further aspects of a method forschedule mimicking, in accordance with embodiments of the currentdisclosure;

FIG. 73 is a schematic diagram depicting an apparatus for schedulemimicking, in accordance with embodiments of the current disclosure;

FIG. 74 is a schematic diagram depicting certain further aspects of anapparatus for schedule mimicking, in accordance with embodiments of thecurrent disclosure;

FIG. 75 is a flowchart depicting a method for schedule mimicking, inaccordance with embodiments of the current disclosure;

FIG. 76 is a flowchart depicting certain further aspects of a method forschedule mimicking, in accordance with embodiments of the currentdisclosure;

FIG. 77 is a schematic diagram depicting an agglomerate network forgenerating schedule data for schedule mimicking, in accordance withembodiments of the current disclosure;

FIG. 78 is a schematic diagram depicting certain further aspects of anagglomerate network for generating schedule data for schedule mimicking,in accordance with embodiments of the current disclosure;

FIG. 79 is a block diagram depicting a non-transitory computer-readablemedium for schedule mimicking, in accordance with embodiments of thecurrent disclosure;

FIG. 80 is a block diagram depicting certain further aspects of anon-transitory computer-readable medium for schedule mimicking, inaccordance with embodiments of the current disclosure;

FIG. 81 is a block diagram depicting a non-transitory computer-readablemedium for schedule mimicking, in accordance with embodiments of thecurrent disclosure;

FIG. 82 is a block diagram depicting certain further aspects of anon-transitory computer-readable medium for schedule mimicking, inaccordance with embodiments of the current disclosure;

FIG. 83 is a schematic diagram depicting an apparatus for a bootstrapscheduler, in accordance with an embodiment of the current disclosure;

FIG. 84 is a schematic diagram depicting certain further aspects of anapparatus for a bootstrap scheduler, in accordance with an embodiment ofthe current disclosure;

FIG. 85 is a flowchart depicting a method for a bootstrap scheduler, inaccordance with an embodiment of the current disclosure;

FIG. 86 is a flowchart depicting certain further aspects of a method fora bootstrap scheduler, in accordance with an embodiment of the currentdisclosure;

FIG. 87 is a schematic diagram depicting an apparatus for a bootstrapscheduler, in accordance with an embodiment of the current disclosure;

FIG. 88 is a schematic diagram depicting certain further aspects of anapparatus for a bootstrap scheduler, in accordance with an embodiment ofthe current disclosure;

FIG. 89 is a flowchart depicting a method for a bootstrap scheduler, inaccordance with an embodiment of the current disclosure;

FIG. 90 is a flowchart depicting certain further aspects of a method fora bootstrap scheduler, in accordance with an embodiment of the currentdisclosure;

FIG. 91 is a block diagram depicting a non-transitory computer-readablemedium for a bootstrap scheduler, in accordance with an embodiment ofthe current disclosure;

FIG. 92 is a block diagram depicting certain further aspects of anon-transitory computer-readable medium for a bootstrap scheduler, inaccordance with an embodiment of the current disclosure;

FIG. 93 is a block diagram depicting a non-transitory computer-readablemedium for a bootstrap scheduler, in accordance with an embodiment ofthe current disclosure;

FIG. 94 is a block diagram depicting certain further aspects of anon-transitory computer-readable medium for a bootstrap scheduler, inaccordance with an embodiment of the current disclosure;

FIG. 95 is a schematic diagram depicting an apparatus for a bootstrapscheduler, in accordance with an embodiment of the current disclosure;

FIG. 96 is a schematic diagram depicting certain further aspects of anapparatus for a bootstrap scheduler, in accordance with an embodiment ofthe current disclosure;

FIG. 97 is a flowchart depicting a method for a bootstrap scheduler, inaccordance with an embodiment of the current disclosure;

FIG. 98 is a flowchart depicting certain further aspects of a method fora bootstrap scheduler, in accordance with an embodiment of the currentdisclosure;

FIG. 99 is a block diagram depicting a non-transitory computer-readablemedium for a bootstrap scheduler, in accordance with an embodiment ofthe current disclosure;

FIG. 100 is a block diagram depicting certain further aspects of anon-transitory computer-readable medium for a bootstrap scheduler, inaccordance with an embodiment of the current disclosure;

FIG. 101 is a flowchart depicting another method for a bootstrapscheduler, in accordance with embodiments of the current disclosure;

FIG. 102 is a schematic diagram of a self-organizing agglomeratenetwork, in accordance with embodiments of the current disclosure;

FIG. 103 is a schematic diagram of an apparatus for self-organizing anagglomerate network, in accordance with embodiments of the currentdisclosure;

FIG. 104 is a flowchart depicting a method for self-organizing anagglomerate network, in accordance with embodiments of the currentdisclosure;

FIG. 105 is a schematic diagram of another apparatus for self-organizingan agglomerate network, in accordance with embodiments of the currentdisclosure;

FIG. 106 is a flowchart depicting another method for self-organizing anagglomerate network, in accordance with embodiments of the currentdisclosure;

FIG. 107 is a schematic diagram of an agglomerate network for extendedhorizon scheduling, in accordance with embodiments of the currentdisclosure;

FIG. 108 is a schematic diagram of an apparatus for extended horizonscheduling, in accordance with embodiments of the current disclosure;

FIG. 109 is a flowchart depicting a method for extended horizonscheduling, in accordance with embodiments of the current disclosure;

FIG. 110 is another flowchart of the method of FIG. 109 , in accordancewith embodiments of the current disclosure;

FIG. 111 is a schematic diagram of another apparatus for extendedhorizon scheduling, in accordance with embodiments of the currentdisclosure;

FIG. 112 is another schematic diagram of the apparatus of FIG. 111 , inaccordance with embodiments of the current disclosure;

FIG. 113 is flowchart depicting a method for extended horizonscheduling, in accordance with embodiments of the current disclosure;

FIG. 114 is another flowchart depicting the method of FIG. 113 , inaccordance with embodiments of the current disclosure;

FIG. 115 is a schematic diagram of another agglomerate network forextended horizon scheduling, in accordance with embodiments of thecurrent disclosure;

FIG. 116 is another schematic diagram of the agglomerant network of FIG.115 , in accordance with embodiments of the current disclosure;

FIG. 117 is a schematic diagram of another apparatus for extendedhorizon scheduling, in accordance with embodiments of the currentdisclosure;

FIG. 118 is another schematic diagram of the apparatus of FIG. 117 , inaccordance with embodiments of the current disclosure;

FIG. 119 is a flowchart depicting another method for extended horizonscheduling, in accordance with embodiments of the current disclosure;

FIG. 120 is another flowchart of the method of FIG. 119 , in accordancewith embodiments of the current disclosure;

FIG. 121 is a schematic diagram depicting an austere event schedulingapparatus, in accordance with embodiments of the current disclosure;

FIG. 122 is a block diagram depicting another austere event schedulingapparatus, in accordance with embodiments of the current disclosure;

FIG. 123 is a flowchart depicting a method for adjusting a scheduleresponsive to an austere event, in accordance with embodiments of thecurrent disclosure;

FIG. 124 is a flowchart depicting another method for adjusting aschedule responsive to an austere event, in accordance with embodimentsof the current disclosure;

FIG. 125 is a schematic diagram depicting an agglomerate network forgenerating schedule data which includes an austere event circuit, inaccordance with embodiments of the current disclosure;

FIG. 126 is a schematic diagram depicting further aspects of theagglomerate network of FIG. 125 , in accordance with embodiments of thecurrent disclosure;

FIG. 127 is a block diagram of a non-transitory computer-readable mediumthat stores instructions that adapt at least one processor to interpretand adjust schedule data responsive to an austere event, in accordancewith embodiments of the current disclosure;

FIG. 128 is a block diagram depicting further aspects of thenon-transitory computer-readable medium of FIG. 127 , in accordance withembodiments of the current disclosure;

FIG. 129 is a flowchart depicting yet another method of adjusting aschedule responsive to an austere event, in accordance with embodimentsof the current disclosure;

FIG. 130 is a flowchart depicting further aspects of the method of FIG.129 , in accordance with embodiments of the current disclosure;

FIG. 131 depicts an apparatus for enabling workers compete for upcomingshifts, in accordance with embodiments of the current disclosure;

FIG. 132 depicts portions of an apparatus for letting workers competefor upcoming shifts, in accordance with embodiments of the currentdisclosure;

FIG. 133 depicts worker properties, in accordance with embodiments ofthe current disclosure;

FIG. 134 depicts shift properties, in accordance with embodiments of thecurrent disclosure;

FIG. 135 depicts a method for enabling bidding on upcoming shifts, inaccordance with embodiments of the current disclosure;

FIG. 136 depicts instructions for a processor, in accordance withembodiments of the current disclosure;

FIG. 137 depicts an apparatus for enabling workers compete for upcomingshifts, in accordance with embodiments of the current disclosure;

FIG. 138 schematically depicts an apparatus for connector biasing, inaccordance with embodiments of the current disclosure;

FIG. 139 schematically depicts an apparatus for connector biasing, inaccordance with embodiments of the current disclosure;

FIG. 140 schematically depicts an apparatus for connector biasing, inaccordance with embodiments of the current disclosure;

FIG. 141 schematically depicts an apparatus for connector biasing, inaccordance with embodiments of the current disclosure;

FIG. 142 depicts a flowchart for operations directed to connectorbiasing, in accordance with embodiments of the current disclosure;

FIG. 143 depicts a flowchart for operations directed to connectorbiasing, in accordance with embodiments of the current disclosure;

FIG. 144 schematically depicts an apparatus for connector biasing withconstrained data, in accordance with embodiments of the currentdisclosure;

FIG. 145 schematically depicts an apparatus for connector biasing withconstrained data, in accordance with embodiments of the currentdisclosure;

FIG. 146 schematically depicts an apparatus for connector biasing withconstrained data, in accordance with embodiments of the currentdisclosure;

FIG. 147 schematically depicts an apparatus for connector biasing withconstrained data, in accordance with embodiments of the currentdisclosure;

FIG. 148 schematically depicts an apparatus for connector biasing withconstrained data, in accordance with embodiments of the currentdisclosure;

FIG. 149 schematically depicts an apparatus for connector biasing withconstrained data, in accordance with embodiments of the currentdisclosure;

FIG. 150 depicts a procedure for connector biasing with constraineddata, in accordance with embodiments of the current disclosure;

FIG. 151 depicts a procedure for connector biasing with constraineddata, in accordance with embodiments of the current disclosure;

FIG. 152 schematically depicts an agglomerate network, in accordancewith embodiments of the current disclosure;

FIG. 153 schematically depicts an agglomerate network, in accordancewith embodiments of the current disclosure;

FIG. 154 schematically depicts an agglomerate network, in accordancewith embodiments of the current disclosure;

FIG. 155 depicts a procedure of an agglomerate network, in accordancewith embodiments of the current disclosure;

FIG. 156 depicts a procedure of an agglomerate network, in accordancewith embodiments of the current disclosure;

FIG. 157 depicts a procedure of an agglomerate network, in accordancewith embodiments of the current disclosure;

FIG. 158 depicts a procedure of an agglomerate network, in accordancewith embodiments of the current disclosure;

FIG. 159 depicts a procedure of an agglomerate network, in accordancewith embodiments of the current disclosure;

FIG. 160 depicts a procedure of an agglomerate network, in accordancewith embodiments of the current disclosure;

FIG. 161 depicts a procedure of an agglomerate network, in accordancewith embodiments of the current disclosure;

FIG. 162 is a block diagram depicting an apparatus for schedulespreading, in accordance with an embodiment of the current disclosure;

FIG. 163 is a block diagram depicting certain further aspects of anapparatus for schedule spreading, in accordance with an embodiment ofthe current disclosure;

FIG. 164 is a flowchart depicting a method for schedule spreading, inaccordance with an embodiment of the current disclosure;

FIG. 165 is a flowchart depicting certain further aspects of a methodfor schedule spreading, in accordance with an embodiment of the currentdisclosure;

FIG. 166 is a block diagram depicting an agglomerate network forschedule spreading, in accordance with an embodiment of the currentdisclosure;

FIG. 167 is a block diagram depicting certain further aspects of anagglomerate network for schedule spreading, in accordance with anembodiment of the current disclosure;

FIG. 168 is a schematic diagram of an apparatus having agglomeratenetwork circuits and connector circuits, in accordance with embodimentsof the current disclosure;

FIG. 169 is a flowchart depicting a method for an agglomerate network,in accordance with embodiments of the current disclosure;

FIG. 170 is a schematic diagram of another apparatus having agglomeratenetwork circuits and connectors circuits, in accordance with embodimentsof the current disclosure;

FIG. 171 depicts rule-length-encoded (RLE) representations of schedules,in accordance with embodiments of the current disclosure;

FIG. 172 depicts the architecture of the convolutional neural network asdetermined by a scheme, in accordance with embodiments of the currentdisclosure;

FIG. 173 depicts a discriminator architecture determination table, inaccordance with embodiments of the current disclosure;

FIG. 174 depicts a generator architecture determination table, inaccordance with embodiments of the current disclosure;

FIGS. 175 and 176 depict discriminator architecture determinationgraphs, in accordance with embodiments of the current disclosure;

FIG. 177 depicts an example apparatus to improve network performance;

FIG. 178 depicts an example of an agglomerate network for generatingschedule data;

FIG. 179 depicts an example method for improving the performance of anagglomerate network;

FIG. 180 depicts an example of processor actions resulting frominstructions stored on a non-transitory computer-readable medium;

FIG. 181 is a block diagram depicting an apparatus for generatingmultiple schedules, in accordance with an embodiment of the presentdisclosure;

FIG. 182 is a block diagram depicting certain further aspects of anapparatus for generating multiple schedules, in accordance with anembodiment of the present disclosure;

FIG. 183 is a flow diagram depicting a method for generating multipleschedules, in accordance with an embodiment of the present disclosure;

FIG. 184 is a flow diagram depicting certain further aspects of a methodfor generating multiple schedules, in accordance with an embodiment ofthe present disclosure;

FIG. 185 is a flow diagram depicting certain further aspects of a methodfor generating multiple schedules, in accordance with an embodiment ofthe present disclosure;

FIG. 186 is a block diagram depicting a non-transitory computer-readablemedium for generating multiple schedules, in accordance with anembodiment of the present disclosure; and

FIG. 187 is a block diagram depicting certain further aspects of anon-transitory computer-readable medium for generating multipleschedules, in accordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION

Without limitation to any other aspect of the present disclosure,aspects of the disclosure herein improve aspects of scheduling. Methodsand systems described herein provide various improvements to schedulingby utilizing an agglomerate framework (also referred to as theagglomerate model herein). In one aspect, the agglomerate frameworkenables the utilization of a plurality of scheduling algorithms foridentifying schedules. As used herein, the term schedule may includetime-sequences, e.g., an ordered list or sequence of events, tasks,and/or shifts which may correspond to dates, days of the week, times ofthe day, etc. The plurality of scheduling algorithms may provide moreaccurate, higher quality, useful, and/or optimum schedules for a widevariety of situations, target types, requirements, and the like. Themethods and systems described herein improve the technical field ofscheduling. In one aspect the field of scheduling is improved byenabling faster and more accurate adaptations of schedules to changingenvironments and/or requirements. In another aspect, the field ofscheduling is improved by enabling an automated system to capture andrecreate subtle qualitative features or aspects of schedules that couldnot be previously captured using an automated system.

The systems and methods described herein provide various technicalbenefits and improvements over known systems and methods. In one aspect,a scheduling system is improved by providing a structure and frameworkwhere different scheduling methods may be used. In one aspect, thescheduling system enables the use of different scheduling modules and/orscheduling algorithms without modification of the modules andalgorithms. In one aspect, the scheduling system enables the applicationof scheduling modules and algorithms for scenarios and schedulingconfigurations for which the scheduling modules and algorithms may nothave been designed for. The scheduling system enables adaptations ofexisting scheduling modules and/or algorithms for new applications andscenarios thereby reducing scheduler development time, cost, andrequired resources since existing modules and algorithms may be reusedand adapted.

The systems and methods described herein provide various technicalbenefits and improvements to computer technology. In one aspect, themethods and systems described herein provide for parallelization and/ordistribution of computing tasks. The systems and methods enable schedulecomputation with a plurality of interconnected modules that may bedistributed over different computing hardware. In one aspect, thecomputations of each module may be less complex and require fewercomputation resources than a traditional monolithic implementation thatmay require large memory and computation resources. In one aspect, themethods and systems described herein provide for improvement to computertechnology by enabling adjustable computer resource utilization. Thesystems and methods described herein include a modular and iterativestructure that enable adaptable complexity for utilization andadaptation based on compute time, resource, and cost requirements and/orlimitations. In one aspect complexity may be adjusted by adding orremoving modules related to external data and/or adjusting the number ofiterations used in computations.

In another aspect, the methods and systems described herein provide forimprovement to computer technology by adapting and formatting databetween different modules and/or algorithms used for computation. Themethods and system include the use of configurable connectors that mayadapt and format data outputs of different modules and/or algorithmsthat would normally not be compatible and/or usable between thedifferent modules. In embodiments, various methods of biasing, datacorrection, and monitoring enable data compatibility and use betweenmodules and/or algorithms that may normally not be compatible.

For the purpose of promoting an understanding of the principles of thedisclosure, reference will now be made to the embodiments illustrated inthe drawings and described in the following written specification. It isunderstood that no limitation to the scope of the disclosure is therebyintended. It is further understood that the present disclosure includesany alterations and modifications to the illustrated embodiments andincludes further applications of the principles disclosed herein aswould normally occur to one skilled in the art to which this disclosurepertains. The present disclosure describes systems, methods, andapparatuses using connectors that provide for architectures fornetworked, autonomous, agglomerated resource utilization modelers.Certain embodiments herein may provide for results, e.g., a schedulerand/or a schedule, to a user as a secondary step that utilizes anagglomerated model. Certain embodiments may turn all forecasted datainto a schedule.

As used herein, a schedule may refer to work schedules where employeesor other personnel and/or resources are scheduled for work or otherduties at certain locations and/or times of the day. Schedules mayinclude one or more different fixed schedules, part-time schedules,shift schedules, and the like. In some embodiments, schedules mayinclude scheduling of one or more resources, such as physical goods,virtual goods, physical locations and virtual locations. Schedules mayfurther include non-work schedules such as personal schedules, educationschedules, organizational schedules, reservation schedules, appointmentschedules, and any other type of schedule or combination thereof.

In embodiments, a schedule may be generated by a scheduler, alsoreferred to herein as a “scheduling circuit/module/model”, “schedulercircuit/module/model”. In certain aspects of the current disclosure, ascheduler and/or a scheduling circuit may be referred to herein as atime-sequencer and/or a time-sequencing circuit. A scheduler may beconfigured to determine how to commit resources (such as employees)between a plurality of possible timeslots and/or tasks. In embodiments,a scheduler may be one or more of a computer algorithm, system, and/or adevice that assigns resources according to one or more constraints of aresource and/or constraints on a task/timeslot. In embodiments, ascheduler may be configured to identify schedules by evaluating penaltyfunctions according to the constraints of the resources and/or theconstraints on the task/timeslot. In embodiments, various types ofschedulers may be used to generate a schedule and may include a neuralnetwork, a deterministic algorithm, a brute force algorithm, astatistical algorithm, a probabilistic algorithm, and/or the like.

An agglomerate network may be a collection of various types ofcircuits/modules/models, as described herein, e.g., scheduler circuits,connector circuits, schedule analysis circuits, etc. As described ingreater detail herein, the various circuits of an agglomerate networkmay be connected together, e.g., so that data can flow between them, viaone or more connectors, also referred to herein as “connectorcircuit/modules”.

Schedule analysis circuits/models/modules may analyze schedule data withrespect to an object or for a purpose, e.g., determining if a generatedschedule is unfair to a particular employee. Embodiments of scheduleanalysis circuits may generate data used to determine biases and/orselect inputs and/or outputs for propagating data through an agglomeratenetwork.

Schedule adjuster circuits/models/modules include circuits thatinterpret schedule data and make an adjustment to the schedule data.

An entity, as described herein, may be any entity or person, a company,a single plant, a number of co-located working locations, e.g., multipleplants within a common geographic region, a single manufacturing linewith multiple stations, a set of manufacturing lines with similar skillrequirements, a retail location, a restaurant, and the like wheremultiple workers may perform shift work. In certain aspects of thecurrent disclosure, the term user may refer to an entity.

An employee, as described herein, may include any worker, whether paidor unpaid. An employee, as described, is not to be limited to the legaldefinition of an “employee” and is to include any individual who followsa schedule, either as part of a contracted or work-related obligation oras a volunteer. Non-limiting examples of employees, as contemplated bythe current disclosure, include legal employees, contractors, interns,volunteers, etc. In certain aspects, an employee may include a user of aschedule.

A shift, as used herein, may include a unit and/or block of time forwhich one or more employees may be assigned to work, e.g., a scheduleshift. A shift may include (or be made up of) other shifts, alsoreferred to herein as sub-shifts.

Non-limiting examples of contracts, as used herein, include: writtencontracts, oral contracts, formal contracts, and/or informal contracts.A contract, as used herein, is not limited to the legal definition ofthe term “contract” and, as such, contracts related to embodiments ofthe current disclosure need not require consideration. As such,contracts, as used herein, may refer to any agreement, understanding, orpromise, e.g., a set of terms. Contracts, as used herein, may be basedon course of a dealing and/or may be de facto.

Embodiments of the system, as disclosed herein, may also provide forpattern detection of preferences, rules and/or constraints. Thedetection may use artificial intelligence (AI) and/or machine learning(ML) to observe sets or histories of schedules to detect when patternsmay occur. In some embodiments, schedules or schedule edits over timemay be observed to contain one or more patterns. For example, detectingor determining that a particular employee prefers not to work a certainshift even if it is not explicitly mentioned in their preferences. Asanother example, embodiments of the current disclosure may detect ordetermine certain equivalences, e.g., a workload can be handled by four(4) new employees or three (3) experienced employees. As anotherexample, embodiments of the current disclosure may observe that when twoparticular employees work together, their output may be particularlyhigh or low. Embodiments of the current disclosure may also build and/ormodify schedules, constraints, suggestions, requests for confirmations,profiles, HR employee performance information, etc., with these observedpatterns.

In embodiments, different schedulers may generate different schedulesfor the same constraints. In some cases, different schedulers may beoptimized for different targets, industries, regions, situations, andthe like. In some cases, schedules may be configured for differenttargets, regions, and the like by adjusting penalty functions.

In some embodiments, schedulers may receive external data related toconstraints, events, financial information, and the like. Schedulers mayreceive data from one or more forecasting models such as weather,financial, event, traffic, and/or other models. Differentmodels/modules/circuits may be incorporated into scheduling.

Accordingly, referring now to FIG. 1 , embodiments of the currentdisclosure provide for a platform 100 for generating schedules using anagglomerate network component/module/circuit 110. Embodiments of theplatform 100 also provide for: a marketplace component/module/circuit112 for exchange of schedule related items; a feedbackcomponent/module/circuit 114; a shared employee contractingcomponent/module/circuit 116; and/or an experimentationcomponent/module/circuit 118 for designing and/or executing schedulingand incentive related experiments. The platform 100 may further providefor user interfaces 120, e.g., graphical user interfaces (GUIs); and/orinclude (and/or access) a variety of computing resources 122.

The agglomerate network component 110 may include models 124, schedulegenerators 126, a schedule analysis component 128, connectors 130, abias optimization component 132, and/or other types of agglomeratenetwork modules/circuits, which are described in greater detail herein.Embodiments of the agglomerate network component 110 provide for theformation/generation/assembly of an agglomerate network and/or thegeneration of scheduling data. Aspects of the agglomerate networkcomponent 110 may interface with one or more of the other components ofthe platform 100, e.g., the marketplace component 112, feedbackcomponent 114, shared employee contracting component 116, computingresources 122, experimentation component 118, and/or the user interfaces120.

The marketplace component 112 may include a trend harvesting component134, an incentives component 136, and/or a bids/offers component 138.Embodiments of the marketplace component 112 may provide for employeesto bid on and/or trade schedule shifts where information regardingmarketplace activities, e.g., what is the most desirable shift, isgleaned from the marketplace component 112 and used in theexperimentation 118 and/or agglomerate network 110 components.

The feedback component 114 may include a surveys component 140, anextracted trends component 142, and/or other types of agglomeratenetwork modules/circuits, which are described in greater detail herein.Information collected via the feedback component 114 may be used in theagglomerate network 110 and/or experimentation 118 components.

The shared employee contracting component 116 may include a smartcontracts component 144, and/or other types of agglomerate networkmodules/circuits, which are described in greater detail herein.Embodiments of the shared employee contracting component 116 may seek tooptimize an employee's availability across one or more entities and/orto provide flexibility in scheduling shifts.

The experimentation component 118 may include a schedule experimentationcomponent 146, an incentive experimentation component 148, and/or othertypes of agglomerate network modules/circuits, which are described ingreater detail herein.

The computing resources component 122 may include a cloud servicescomponent 150, a data resources component 152, and/or other types ofagglomerate network modules/circuits, which are described in greaterdetail herein. The data resources component 152 may include a scheduledata component 154, an environmental data component 156, an entity datacomponent 158, an employee data component 160, and/or other types ofagglomerate network modules/circuits, which are described in greaterdetail herein. The cloud services component 150 may include one or moreservers (or access to one or more services) that provide processingservices to execute the methods disclosed herein. In embodiments, thecloud services 150 may include devices incorporating one or more of themodules/circuits disclosed herein, such as those forming the platform100.

As illustrated in FIG. 2 , systems, e.g., the platform 100 (FIG. 1 ),and methods described herein provide for an agglomerate network 200 thatgenerates schedule data 210 via one or more agglomerate networkmodules/circuits, e.g., 212, 214, 216, 218, 220 connected via one ormore connector modules/circuits 222, 224. The agglomerate network mayinclude a plurality of different schedulers, scheduling models, datamodels, forecasting models, and the like. In embodiments, the connectorsmay be configurable. Configurable connectors may enable the agglomeratenetwork 200 to be configured to generate a schedule, e.g., schedule data210, using different combinations of models, 214, 216, 218, 220, anddata. The configuration of the agglomerate network may be selected so asto provide improved performance of a schedule. For example, theagglomerate network 200 may include a scheduler 212 that receivesschedule input data 228 and generates schedule data 210, where theschedule data 210 is altered (e.g., biased, reformatted, etc.) by aconnector 222 based on data generated by a weather model 214 and aholiday sales model 216, where the scheduler 212 itself may not bestructured to incorporate and/or account for weather and/or holidaysales. As further shown in FIG. 2 , a connector, e.g., 222 maymanipulate the input(s) 230 and/or the output(s) 232 of an agglomeratenetwork circuit 212. Thus, as will be appreciated, connectors, asdisclosed here, provide for the incorporation of information into amodule that the module is not structured to use and/or otherwise accountfor without having to edit the module, e.g., edit the module's software.Connectors, as disclosed herein, also provide for the ability to linkdisparate modules together to form an agglomerated network where theresulting agglomerated network produces more accurate scheduling data ascompared to what would be provided by the disparate modulesindividually.

In one aspect, the methods and systems described herein improve theperformance and quality of generated schedules. The performance andquality of a schedule may be determined using various qualitative andquantitative measures. In one example the quality of a schedule may bedetermined by monitoring a schedule across one or more dimensions suchas schedule coverage, absenteeism, work-life balance, feedback fromusers, resource utilization, adequacy of staffing, profit objectives,and the like. As used herein, the terms “target”, “goal”, “aim”, and“intent” may also be used to refer to an objective. Schedules may bescored across a plurality of dimensions to determine a quality metricscore. In one aspect the quality score may be a weighted function of oneor more dimensions of a schedule. In some cases, the weighted functionmay be adjusted for different industry types, enterprise sizes,objectives, and the like. In another example, the performance andquality of a schedule may be determined by monitoring the number,frequency, and/or magnitude of adjustments or changes that are made to aschedule. In some cases, frequent or large changes to a schedule byusers may indicate that the automatically generated schedule did notmeet the schedule requirements and may be scored lower than a schedulethat was not edited by a user.

In embodiments, a configuration of an agglomerate network may beconfigured based on past performance of generated schedules for specifictargets, situations, industries, and the like. In embodiments, theconfiguration of the agglomerate network for generating a schedule maybe adjusted during the schedule generating process based on user input,outputs of one or more models of the agglomerate network and the like.

Accordingly, embodiments of the current disclosure may provide forsystems and methods for autonomously constructing agglomerated resourceutilization models from a set of largely independent, networked models.Notably, embodiments of the systems and methods may actively consumenetworked models and information, and in one aspect enable theautonomous discovery of resource availability, resource constraints,resource condition, future resource scheduling requirements, andadditional related and/or correlated data which may form a set ofagglomerated models and/or data. For example, the systems and methodsmay explore analogous (e.g., similar company) or related (e.g., a parentor child company) resource availability, resource constraints, resourcecondition, future resource scheduling requirements, and additionalrelated and/or correlated data. In addition, or alternatively, thesystems and methods may look for features to use with one or more of theagglomerated networks, models, or schedules. In some embodiments, theautonomous discovery may include a Natural Language Processing (NLP)engine which explores information that may impact the resources, models,constraints, conditions, requirements, and/or schedules. For example, awebsite may advertise a sale adjacent to one of the stores for whichschedules are being created. The sale may affect customer traffic atthat location, and the system will consider this information. In anothercase, a news article may describe a labor shortage in a particular cityof interest to the systems and methods which may affect labor costs inthat location. Using various types of information, embodiments of thesystem may generate and/or update one or more models to be included in aset of agglomerated models and data, where the agglomerated models maydescribe a set of interrelated resource models such as schedules,capacity plans, etc. Interconnecting agglomerate models/circuits maysupport the efficient sharing of more accurate model data by detecting,understanding, and/or correcting for biases in individual or combinedagglomerate model results, and/or by intelligently associating modelconfidences for source, intermediate, and/or final model data. Incontrast to existing technologies, the system and/or methods mayautonomously and/or efficiently improve a set of independent and/ordependent agglomerated resource models across multiple time and featurehorizons to improve quality and confidence.

Embodiments of the current disclosure may include an autonomousagglomerated resource utilization modeler architecture. Accordingly,shown in FIG. 3 is an architecture 300 for an Autonomous AgglomeratedResource Utilization Modeler for an agglomerate network 310, inaccordance with embodiments disclosed herein. As will be appreciated,the level of abstraction shown in FIG. 3 highlights the synergistic andreinforcing relationship between the set of agglomeratedmodels/circuits/modules.

Embodiments of the architecture 300 may include hierarchicalagglomerated resource models 312 including sentiment models 314,capacity modules 316, schedule models 318, event models 320, retentionmodels 322, high-value employee models 324, business models 326,effective incentive models 328, absenteeism models 330, and/or any othermodels described herein or otherwise suitable for schedule generation.In embodiments, the agglomerated models contain a group of cross coupledmodels, which may be developed independently and/or for distinctpurposes. The cross coupled models may be associated in this groupingbecause they generate information useful to the efficient production ofimproved resource scheduling information. The agglomerate models mayrepresent models that include models, systems and methods describedherein and may further include third-party models capable of consuminginformation and/or producing data and information useful to theoperation of other agglomerate models. In embodiments, the agglomeratemodels may individually and/or collectively support the calculation ofresource scheduling and/or interim results across multiple dimensionsincluding, but not limited to, time, region, organizational, and/orlevel of abstraction.

Embodiments of the architecture 300 may further include a hierarchicalfeature propagator (HFP) 332, an autonomous evolution controller 334, asemi-autonomous goal setter 336, a semi-autonomous experiment controller338, an agglomerated input handler 340, an agglomerated metrics analyzer342, and agglomerated output composer 344, external data sources (oraccess to the external data sources) 346, an interactive user interface348, external outputs 350, as resolution determiner 352, and/or othercomponents/modules/circuits described herein.

FIG. 4 depicts additional hierarchical agglomerated resourcemodels/circuits/modules 312 which can include external models 410,primary business models 412, and/or secondary business models 414. Theexternal models 410 may include weather impact models 416, externalevent models 418, and/or unemployment models 420. the primary biasnessmodels 412 may be arranged according to organizational hierarchy 422and/or a temporal hierarchy and include one or more atomic resourceschedulers 426, team schedulers 428, and/or organizational schedulers430. The secondary business models 414 may include sentiment models 432,capacity models 434, retention models 436, controllable event models 438and/or fixed models, e.g., high-value employee models 440, and/orbusiness models 442.

Referring to FIG. 5 , embodiments of the current disclosure provide fora hierarchical feature propagator (HFP) 220100 which may be a type ofmodule/circuit that determines (and/or may constantly determine) whattype of data, e.g., 152 (FIG. 1 ) is needed for a particular schedulingscenario, how to locate the data within an organization having ahierarchical structure (organization chart), and/or determines how tocondition data for use in an agglomerate network 220110 having one ormore agglomerate network modules/circuits 220112, 220114, 220116 andconnectors 220118, 220120 that generate schedule data 220122. Inembodiments, the HFP 220100 may determine which modules/circuits, e.g.,124 (FIG. 1 ), and/or connectors, e.g., 130 (FIG. 1 ), are included inan agglomerate network, e.g., 110 (FIG. 1 ), and/or how the modulesand/or connectors are configured. In embodiments, the HFP 220100 maydetermine how to split up a particular problem and/or scenario betweenmultiple modules (which may work towards a joint solution or competeagainst each other). While the HFP 220100 is depicted in FIG. 5 as beingapart from the agglomerate network 220110, embodiments of the HFP 220100may be incorporated into an agglomerate network, e.g., the HFP 220100may be an agglomerate network circuit/module, e.g., 220112, within theagglomerate network 220110.

Embodiments of the HFP 220100 may combine information from differentlevels of an organization, combine new and old data, e.g., historicaldata, and/or combine data from different regions, e.g., data from USnortheast stores and data from US southwest stores.

Embodiments of the HFP 220100 may be useful for scenarios whereorganizational data may evolve over time, e.g., job and/or positionchanges. Embodiments of the HFP 220100 may identify information fromsimilar franchises within the same company and leverage the data toimprove the accuracy of schedules generated by an agglomerate network,e.g., 220110. In embodiments, the HFP 220100 may have access to anemployee's past employment data, e.g., attendance, performance reports,etc., which may be accomplished via a blockchain, e.g., a digitalresume. For example, a digital resume may contain an employee'sattendance record for one or more past jobs which can be used to predictthe employee's future attendance at their current employer and/or at aprospective employer. In embodiments, the HFP 220100 may determine it iswarranted to combine the results of particular agglomerate networkmodules/circuits of the types described herein. For example, an HFP220100 may determine to combine a prior employment historymodule/circuit, e.g., 220112, with an employee category/typemodule/circuit, e.g., 220114, to predict a doctor's attendance at a newhospital by combining an attendance prediction from their digital resume(a first agglomerate network module/circuit, e.g., 220112) with anattendance prediction based on doctors in general (a second agglomeratenetwork module/circuit, e.g., 220114). In such a scenario, the HFP220100 may configure a connector, e.g., 220120, to bias the results ofthe second module 220114 based on the results of the first module 220112to predict whether the doctor is more or less likely to show up to workthan the average doctor.

In embodiments, the HFP 220100 may control the learning processes ofconnectors 220118, 220120 and/or modules/circuits 220112, 220114, 220116within an agglomerate network 220110. In embodiments, the HFP 220100 maymonitor the modules/circuits 220112, 2201114, 220116, connectors 220118,220120, and/or an entire agglomerate network 220110 to determine if theperformance of one or more modules/circuits 220112, 220114, 220116 issatisfactory, e.g., based on user and/or industry standards. The HFP220100 may adjust the configuration of the agglomerate network 220110for training the connectors 220118, 220120. The HFP 220100 may adjustwhich modules/circuits 220112, 220114, 220116 and/or mix ofmodule/circuit outputs are used to train connectors 220118, 220120. Forexample, historical data may identify schedules that were inaccurate dueto a correlated event (such as a weather event). The HFP 220100 maytrain the connectors 220118, 220120 to include data from an appropriateweather module/circuit to update the connectors 220118, 220120 to takeinto account weather anomalies for future scheduling. In embodiments,the HFP 220100 may be trained to learn how to pick modules/circuit20112, 220114, 220116, connectors 220118, 220120, and/or data sourcesfor use in an agglomerate network 220110 for a given scenario, e.g., theHFP 220100 may “evolve” over time.

In embodiments, the HFP 220100 may adjust what modules/circuits and/or amix of module outputs are used in the network based on what it learnsabout an end user of schedule data generated by the agglomerate network220110. The HFP 220100 may use a hierarchy of modules/circuits and/orconnectors as it learns what modules/circuits may be applicable to thecustomer. For example, at the beginning, e.g., when an HFP is firstactivated, the HFP 220100 may structure the agglomerate network 220110to use high-level or generic modules/circuits. As more information islearned about the end user of the schedule data 220122 (such as specificdata about employee attendance, industry metrics, and the like) morespecific/detailed modules/circuits may be used in the mixing within thenetwork 220110. The HFP 220100 may control what feedback loops areactivated, e.g., loops made by a connector, e.g., 220118 feeding into anupstream agglomerate network circuit/module, e.g., 220112, based on whatthe connector learns about the end user. The training and/orreconfiguration of the network 220110 may be periodic (e.g., hourly,daily, weekly, monthly, yearly) or continuous based on feedback fromschedules, triggers of new data, and the like.

In embodiments, the HFP 220100 determines how to use specific inputs(what mixing of circuit/modules 220112, 220114, 220116 and/or connectors220118, 220120 to use) and, in certain scenarios, not what new data toinclude in the network 220110. In embodiments, the HFP 220100 maycontrol the biasing of mixed output and/or change the mixing ofmodules/circuits 220112, 220114, 220116. In embodiments, the HFP 220100may play a role in determining whether an agglomerate network 220110,and/or a portion thereof, is producing acceptable results or is notworking as intended.

Illustrated in FIG. 6 is an apparatus 220200 embodying one or moreaspects of the HFP 220100 (FIG. 5 ), in accordance with embodiments ofthe current disclosure. The apparatus 220200 may form part of acomputing device that executes an agglomerate network, as disclosedherein, or it may be a device apart from one executing an agglomeratenetwork. The apparatus 220200 may be an agglomerate circuit within anagglomerate network, e.g., 220110 (FIG. 5 ), or apart from anagglomerate network. The apparatus 220200 includes a scenariointerpretation circuit 220212, a scenario analysis circuit 220214, adata analysis circuit 220216, a data source locator circuit 220218, adata retrieval circuit 220220, and a data provisioning circuit 220221.The scenario interpretation circuit 220212 is structured to interpretschedule scenario data 220222. In certain aspects of the currentdisclosure, schedule scenario data may be referred to as time-sequencescenario data. The scenario analysis circuit 220214 is structured toextract a scenario element 220224 from the schedule scenario data2202222. Non-limiting examples of scenario elements 220224 includedates, special events, weather events, geographic data, employee data,business metrics and/or objects, and/or the like. The data analysiscircuit 220216 is structured to determine, based at least in part on theextracted scenario element 220224, a type of data 220226 for inclusionin the generation of schedule data 220122 (FIG. 5 ) corresponding to thescenario data 220222. Non-limiting examples of types of data 220226include weather data, organizational data, business metric data,scheduling data, feedback data, and/or the like. The data source locatorcircuit 220218 is structured to identify a source 220230 of the type ofdata 220226 for inclusion in the generation of the schedule data 220122(FIG. 5 ). The data retrieval circuit 220220 is structured to retrievedata 2202232 from the identified source 220230. Non-limiting examples ofdata sources 220230 include databases operated by an entity operatingapparatus 220200, databases operated by entities other than the entityoperating the apparatus 220200, databases operated by an end user of thegenerated scheduled data 220122 (FIG. 5 ), and/or other data sources.The data provisioning circuit 220221 is structured to transmit theretrieved data 220232.

In embodiments, the type of data 220226 for inclusion in the generationof the schedule data 220122 (FIG. 5 ) relates to an organizationhierarchy and the data retrieval circuit 220220 is further structured tocrawl the organization hierarchy in the identified data source 220230.In embodiments, the retrieved data 220232 includes a relationship(including any association, dependency, or the like) between twoemployees of the organization. In embodiments, the apparatus 220200further includes a data conditioning circuit 220234 structured tocondition the retrieved data 220232 for use by at least one of aconnector circuit, e.g., 220118 (FIG. 5 ), or an agglomerate networkcircuit, e.g., 220112 (FIG. 5 ), prior to transmission of the data220232 via the data provisioning circuit 220221. In embodiments,conditioning of the retrieved data 220232 includes adjusting a format ofthe retrieved data 220232. The adjusted format may correspond to anexpected format of an agglomerate network circuit. In embodiments,conditioning of the retrieved data 220232 includes rearranging theretrieved data 220232. In embodiments, conditioning of the retrieveddata 220232 includes extracting a trend 220235 from the retrieved data220232. Non-limiting examples of the trend 220235 include a decrease insales volume, an increase in customer service complaints, an increase inemployee complaints, an increase in employee turnover, etc. Inembodiments, the retrieved data 220232 is a digital resume of anemployee. The digital resume may be based at least in part on ablockchain (digital ledger).

In embodiments, apparatus 220200 further includes a model identifiercircuit 220236 structured to determine, based at least in part on atleast one of the type of data 220226 for inclusion in the generation ofschedule data 220122 (FIG. 2210 ) or on the retrieved data 220232, anagglomerate network circuit 220238 for inclusion in an agglomeratenetwork. For example, in embodiments, the type of data 220226 and/or theretrieved data 220232 may relate to weather and the model identifiercircuit 220236 may determine that a weather mode circuit should beincluded in the agglomerate network 220110 (FIG. 5 ). In embodiments,the model identifier circuit 220236 may determine that a connectorcircuit/module 220240 should be included in the agglomerate network220110. In embodiments, the model identifier circuit 220236 maydetermine one or more structural relationships, as described herein, forthe connector circuit 220240 that should be included in the agglomeratenetwork 220110, e.g., which agglomerate network circuits and/orconnectors the connector 220240 should be connected to. The modelidentifier circuit 220236 may determine that a combination including anagglomerate network circuit and a connector circuit should be includedin the agglomerate network. In embodiments, the model identifier circuit220236 determines that a combination including two agglomerate networkcircuits should be included in the agglomerate network 220110.

Illustrated in FIG. 7 is a method 220300 for an HFP, in accordance withembodiments of the current disclosure. The method 220300 may beperformed via apparatus 220200 and/or any other computing devicedisclosed herein. The method 220300 includes interpreting, via ascenario interpretation circuit, schedule scenario data 220310; andextracting, via a scenario analysis circuit, a scenario element from theschedule scenario data 220312. The method 220300 further includesdetermining, via a data analysis circuit based at least in part on theextracted scenario element, a type of data for inclusion in thegeneration of schedule data corresponding to the scenario data 220314.The method 220300 further includes identifying, via a data sourcelocator circuit, a source of the type of data for inclusion in thegeneration of the schedule data 220316. The method 220300 furtherincludes retrieving, via a data retrieval circuit, data from theidentified source 220318; and transmitting, via a data provisioningcircuit, the retrieved data 220320.

Embodiments of the current disclosure may also provide for anon-transitory computer-readable medium storing instructions that adaptat least one processor to: interpret schedule scenario data; and extracta scenario element from the schedule scenario data. The storedinstructions may further adapt the at least one processor to determine,based at least in part on the extracted scenario element, a type of datafor inclusion in the generation of schedule data corresponding to thescenario data. The stored instructions may further adapt the at leastone processor to identify a source of the type of data for inclusion inthe generation of the schedule data; retrieve data from the identifiedsource; and transmit the retrieved data.

In embodiments, when data is not available for a particular feature, orto improve the confidence level of an input feature, the HierarchicalFeature Propagator may look to other levels of the hierarchicalconfiguration to provide and/or improve the input. Through interactionswith the Model Input and Output Connectors, the feature propagator maydraw a model's feature input data point from another agglomerate modelrepresenting the same organization directly, from a comparableagglomerate model data point from the same organization, from anothercomparable organization/group, and/or from an averaging or mixture ofthese sources. Further, the hierarchical feature propagator may instructthe output and input connectors to mix or combine data over differenttime scales. In embodiments, the Hierarchical Feature Propagator may mixin results from aggregated departments, franchises, and/or businesses toprevent over-fitting issues when new businesses, locations, departments,etc., are introduced, and there are insufficient data points to reliablytrain the localized models. In certain circumstances, some level ofmixing may be appropriate long-term to prevent localized over-fitting ofsolutions.

In embodiments, mixing may involve adjusting which data sources one ormore connectors receive input from and/or push results to. In otherwords, mixing may include adjusting the connections made via theconnectors in an agglomerate network. Adjusting a connector may alsoinclude adjusting weights corresponding to one or more of its inputs.For example, a connector may draw its input(s) from generic sourceslearned from an aggregate of sources, which might be initially 90%weighted. The connector may also get info about a department and/orlocation, and then shift the bias (weights) to local establishment. Inembodiments, a connector's weight bias might be used for a firstscheduling algorithm at a first time and then shifted to a differentvalue for use by a second scheduling algorithm at the same and/or adifferent time. In embodiments, a connector's weight bias might be usedfor a first scheduling algorithm at a first time and then shifted to adifferent value for use by the same scheduling algorithm at a differenttime. Non-limiting examples warranting a shift in a connector's bias mayinclude emergency situations, detection of out-of-bound results and/orparameters from a prior scheduling operation, etc. In embodiments,mixing may be hierarchical, e.g., mixing may occur at one or more levelswithin a hierarchy of agglomerated networks, e.g., budgeting andscheduling at the same time.

In embodiments, mixing may include pulling constraints and/orpreferences from one hierarchy to another, and/or from one scheduler toanother. Mixing may also include merging constraints from differenthierarchies and optimizing and/or simplifying the merged constraints. Anon-limiting example of mixing may include running a scheduler at onelevel of the hierarchy and producing schedules, and then using thoseschedules as input into a second hierarchy or scheduler (which may havedifferent algorithms, constraints, and weights) and seeing the resultingschedule(s), wherein the best scoring schedule from the second run maybe selected for implementation. Mixing may also include analyzing and/ormerging objective functions from different hierarchy levels. Forexample, objective function values for individual stores may beaggregated at a higher level such as a region. This aggregation mayoccur by adding the individual scores, taking the maximum, the average,etc., and using one or more of these values as input into a higher-levelobjective function. As another example, a higher-level objectivefunction may override a lower-level objective function in some cases,e.g., an optimal region objective function may override one or morestores' optimal objective functions (below it in the hierarchy). In yetanother example, an overridden store's objective function may rememberthis from a first schedule and increase its objective function value sothat it does not override on the next (or other future) week's score.Similarly, a store that had its optimal, or near-optimal scheduleaccepted, may have its objective function value decreased so it does notalways get its way over the store which had its objective function valueoverridden by the higher-level objective function. Thus, some stores donot always get their way over another store's preferred schedules. Inyet another example, the system may observe which constraints lead tobetter employee moral/turnover, e.g., if one store has a constraint thatan employee should work either Friday or Saturday but not both, and itmight be observed to have lower attrition in a category of employees,e.g., high school students. In embodiments, the system may experimentwith this constraint across other stores to see if it improves attritionin that category of employee.

Embodiments may include an autonomous evolution controller. Embodimentsmay provide for a schedule flexor, e.g., the autonomous evolutioncontroller, and/or other components of the system described herein, mayuse an agglomerate network to automatically monitor human resource (HR)data to detect when an employee has a “life event” that warrants themworking reduced and/or modified hours possibly at the expense of otheremployees.

Embodiments of the current disclosure may provide for incentive-basedscheduling, e.g., an agglomerated network may use artificialintelligence (AI) to determine how to incentivize employees to acceptand complete scheduled shifts. Incentives may be provided to employeesto accept and/or make themselves available for particular shifts. Someembodiments may provide for iterative incentive development and/orprovisioning, wherein incentives may get better on each iterationdepending on how urgent the situation is. Embodiments may also foregooffering and/or improving incentives where the agglomerate networkdetects a pattern by employees “holding out” to accept a schedule toimprove their incentives. Embodiments of the current disclosure mayintegrate incentive-based scheduling with an HR recruiting tool. Forexample, a first company may not be able to reliably fill a schedulewith a first set of schedule attributes, and a second company may havean employee who wants certain scheduling attributes that match the firstset of schedule attributes.

Embodiments of the current disclosure include examples of systems andmethods that use artificial intelligence (AI) to determine how toincentivize employees to accept and complete scheduled shifts.Embodiments may provide for an interactive process, with incentivesimproving/escalating/increasing in value on each iteration, depending onhow urgent the situation is. Embodiments may be integrated with a humanresource (HR) recruiting tool/platform and/or form part ofcomponent/modules 136, 114, and/or 116 (FIG. 1 ). For example, a firstcompany may not be able to reliably fill a schedule with a first set ofschedule attributes/properties, but a second company may have anemployee who wants a certain first set of schedule attributes (and maybe unable to get them reliably at the second company). Embodiments mayprovide for employees to provide feedback on incentives, which may takethe form of a rating.

In embodiments, the incentives may be structured to encourage anemployee to work extra hours on a shift for which they are alreadyscheduled. In embodiments, the incentives may be structured to encouragean employee to work a portion of a shift, e.g., relieving a coworker forpart of a shift and/or supplementing the coworker to make the shifteasier. In embodiments, a shift may be a block of time and may be madeup of smaller shifts. Shifts may be single occurrences or recurring. Therecurrence may be daily, weekly, yearly, etc. In embodiments, theincentives may be structured to encourage an employee to take aparticular role on a shift, e.g., a managerial role, running aparticular machine, etc. A non-limiting use case may be a scenarioconcerning a garbage truck, in which the driver position has theincentive of being paid more, but also requires more work, and in whichan employee can select to be the driver or not for a particular shift.

Embodiments of the incentive-based scheduler may be amodule/circuit/model that receives inputs, e.g., a schedule and/or otherdata, e.g., biases, as a direct input, e.g., the incentive-basedscheduler acts as a standalone module; as a direct input to anagglomerate network, e.g., without use of connectors; and/or fromconnectors, e.g., the incentive-based scheduler is one of a plurality ofmodules within an agglomerate network. For example, the incentive-basedscheduler may be a module within an agglomerate network that receives aschedule (e.g., either directly as input to the agglomerate network orfrom a schedule generation module in the agglomerate network) andevaluates whether the schedule warrants incentives tied to particularshifts in the schedule. The output of the incentive-based schedulermodule (e.g., a schedule with associated incentives) may be passed toother modules in the agglomerate network for evaluation. A revisedversion of the schedule, for example, made by the other modules in theagglomerate network, may be passed back into the incentive-basedscheduler module for revaluation by the incentive-based schedulermodule. The connections between the incentive-based scheduler module andthe various other modules of the agglomerate network may beaccomplished, for example, via connectors.

Referring to FIG. 8 , an apparatus 180100 may be provided. The apparatus180100 includes a schedule interpretation circuit 180102, a shiftanalysis circuit 180104, an incentivizer circuit 180106, and anincentive provisioning circuit 180108. The schedule interpretationcircuit 180102 is structured to interpret schedule data 180110. Theshift analysis circuit 180104 is structured to analyze the schedule data180110 and identify a shift 180112. The incentivizer circuit 180106 isstructured to determine incentive data 180114 for the shift 180112. Theincentive provisioning circuit 180108 is structured to transmit 180116the incentive data.

Referring to FIGS. 9-11 , certain further aspects of the apparatus180100 are described following, any one or more of which may be presentin certain embodiments. In certain embodiments, the incentivizer circuit180106 may be further structured to assign an employer value 180202 tothe shift, and the determination of the incentive data 180114 may befurther based at least in part on the employer value 180202. In certainembodiments, the incentivizer circuit 180106 may be further structuredto: compare an employee value 180204 to the employer value 180202 anddetermine the incentive data 180114 based at least in part on adifference 180206 between the employee value 180204 and the employervalue 180202. The employee value 180204 and the employer value 180202may be based at least in part on a common scale, e.g., a scale from one(1) to one hundred (100). For example, a shift that has a high employervalue 180202, e.g., ‘80’ and a low employee value 180204, e.g., ‘10’ mayresult in a high value inventive for the shift. Conversely, a shift thathas a low employer value 180202, e.g., ‘5’, and a high employee value180204, e.g., ‘90’ may result in no incentive for the shift, or a lowvalue inventive for the shift. In embodiments, the weighting of anemployer value 180202 may be given a higher weighting than the employeevalue 180204. For example, a shift with an employer 180202 value of ‘30’and an employee value 180204 of ‘5’ may result in no incentive and/orlittle incentive even though the employee is unlikely to try to fill theshift.

In certain embodiments, the incentive data 180114 may correspond to oneof a plurality of possible incentives 180208, each corresponding to adistinct incentive value 180210 that shares a common scale 180212 withthe employee value 180204 and the employer value 180202, such that theplurality of possible incentives 180208 has an increasing value ordering180214. For example, the employer value 180202, the employee value180204, and the inventive value 180210 may be based on a common scale ofone (1) to one hundred (100), where high value incentives may be given avalue of >80 and low value incentives may be given a value of <20. Thus,a shift with a high employer value 180202, e.g., ‘95’, and a lowemployee value 180204, e.g., ‘5’, may result in a high value incentive,e.g., ‘97’, which may equate to triple overtime pay.

The apparatus may further include an urgency analysis circuit 180302structured to determine urgency data 180304 of the shift 180112 based atleast in part by analyzing the schedule data 180110. The incentivizercircuit 180106 may be further structured to determine the incentive data180110 based at least in part on the urgency data 180304. The apparatusmay further include an urgency interpretation circuit 180306 structuredto interpret urgency data 180304. The incentivizer circuit 180106 may befurther structured to determine the incentive data 180110 based at leastin part on the urgency data 180304. In embodiments, the urgency data180304 may be based on a common scale with the employer value 180202,employee value 180204, and/or the inventive value 180210, e.g., a scaleof one (1) to one hundred (100). For example, shifts that have a highurgency 180304, e.g., ‘>80’, and a high employer value 180202, e.g.,“‘>'80’, may result in a high value incentive, e.g., ‘>80’.

In certain embodiments, the urgency data 180304 may be generated by auser 180308. In certain embodiments, the urgency data may be generatedby an agglomerate network circuit 180310 of an agglomerate network180312. In certain embodiments, the incentive data may correspond to oneor more of a plurality of incentives 180314, the plurality of incentives180314 including at least one of: additional pay 180316, additional timeoff 180318, reward points 180320, employee rating points 180322, orcurrency 180324 for a schedule marketplace. In certain embodiments, theemployee value 180204 may be based at least in part on employee feedback180326 or an insight 180328 determined from a schedule marketplace180330. Employee feedback 180326 may be provided via a responsivescheduler and/or a scheduling marketplace, e.g., 112 (FIG. 1 ), asdisclosed herein.

In certain embodiments, the incentivizer circuit 180106 may be furtherstructured to adjust the incentive data 180114 after a first period oftime 180402 to increase a value of an incentive 180404 corresponding tothe incentive data 180114. In certain embodiments, the incentivizercircuit 180106 may be further structured to iteratively adjust theincentive data 180114, and the adjustments may increase with eachiteration. In certain embodiments, after a second period of time 180406,the incentives may be at least one of: decreased, eliminated, ordropped. In certain embodiments, the second period of time 180406 may beblocked from view, e.g., from employees, for example, to discourage theemployees delaying in accepting shifts to inflate the incentives. Incertain embodiments, the second period of time 180406 may be random. Incertain embodiments, the incentivizer circuit 180106 may be furtherstructured to determine that the shift 180112 cannot be voluntarilyfulfilled after a third period of time 180408. In certain embodiments,an employee 180410 may be selected to fill the shift 180112 after thethird period of time 180408. In certain embodiments, the selection maybe via user input 180412. In certain embodiments, the selection may bebased at least in part on artificial intelligence (AI) 180414, which maybe configured to select the employee 180410 to fill the shift 180112 byoptimizing a variety of parameters such as mitigating turnover,minimizing costs, maximizing employee harmony, e.g., teamwork, etc.

Referring to FIG. 12 , a method 180500 for incentive-based scheduling isshown, in accordance with embodiments of the current disclosure. Themethod 180500 may be performed via apparatus 180100 and/or any othercomputing device disclosed herein. The method 180500 includesinterpreting, via a schedule interpretation circuit, schedule data180502, analyzing, via a shift analysis circuit, the schedule data180504, identifying, via the shift analysis circuit and based at leastin part on the analysis of the schedule data, a shift 180506,determining, via an incentivizer circuit, incentive data for the shift180508, and transmitting, via an incentive provisioning circuit, theincentive data 180510.

Referring to FIGS. 13-14 , certain further aspects of the method 180500are described following, any one or more of which may be present incertain embodiments. For example, in certain embodiments, the method180500 may further include assigning an employer value to the shift180602, and determining the incentive data based at least in part on theemployer value 180604. In certain embodiments, the method may furtherinclude comparing an employee value to the employer value 180606, anddetermining the incentive data based at least in part on a differencebetween the employee value and the employer value 180608. In certainembodiments, the incentive data may correspond to one of a plurality ofpossible incentives, each corresponding to a distinct incentive valuethat shares a common scale with the employee value and the employervalue, such that the plurality of possible incentives has an increasingvalue ordering.

In certain embodiments, the method may further include determiningurgency data of the shift based at least in part by analyzing theschedule data 180610, and determining the incentive data based at leastin part on the urgency data 180612. In certain embodiments, the methodmay further include interpreting urgency data 180614, and determiningthe incentive data based at least in part on the urgency data 180616. Incertain embodiments, the urgency data may be generated by a user 180618.In certain embodiments, the urgency data may be generated by anagglomerate network circuit of an agglomerate network 180620.

In certain embodiments, the incentive data may correspond to one or moreof a plurality of incentives, the plurality of incentives including atleast one of: additional pay, additional time off, reward points,employee rating points, or currency for a schedule marketplace. Incertain embodiments, the employee value may be based at least in part onemployee feedback or an insight determined from a schedule marketplace.

In certain embodiments, the method may further include adjusting theincentive data after a first period of time to increase a value of anincentive corresponding to the incentive data 180702. In certainembodiments, the method may further include iteratively adjusting theincentive data 180704, and increasing the adjustments with eachiteration 180706. In certain embodiments, the method may furtherinclude, after a second period of time, at least one of: decreasing,eliminating, or dropping the incentives 180708. In certain embodiments,the second period of time may be blocked from view. In certainembodiments, the second period of time may be random. In certainembodiments, the method may further include determining that the shiftcannot be voluntarily fulfilled after a third period of time 180710. Incertain embodiments, the method may further include selecting anemployee to fill the shift after the third period of time 180712. Incertain embodiments, the selection may be via user input. In certainembodiments, the selection may be based at least in part on artificialintelligence.

Referring to FIG. 15 , an apparatus 180800 for inventive-basedscheduling, in accordance with embodiments of the current disclosure, isshown. The apparatus 180800 includes a schedule interpretation circuit180802, a shift analysis circuit 180804, an incentivizer circuit 180806,and an incentive provisioning circuit 180808. The scheduleinterpretation circuit 180802 is structured to interpret schedule data180810. The shift analysis circuit is structured to analyze the scheduledata 180810, identify a shift 180812, and assign an employee value180818 to the shift 180812. The incentivizer circuit 180806 isstructured to determine incentive data 180814 for the shift 180812 basedat least in part on the employee value 180818. The incentiveprovisioning circuit 180808 is structured to transmit 180816 theincentive data 180814.

Referring to FIG. 16 , certain further aspects of the apparatus 180800are described following, any one or more of which may be present incertain embodiments. In certain embodiments, the incentivizer circuit180806 may be further structured to assign an employer value 180902 tothe shift 180812, and the determination of the incentive data 180814 maybe further based at least in part on the employer value 180902. Incertain embodiments, the apparatus 180800 may further include an urgencyanalysis circuit 180904 structured to determine urgency data 180906 ofthe shift 180812 based at least in part by analyzing the schedule data180810. The incentivizer circuit may be further structured to determinethe incentive data 180814 based at least in part on the urgency data180906. In certain embodiments, the apparatus 180800 may further includean urgency interpretation circuit 180908 structured to interpret urgencydata 180906. The incentivizer circuit 180806 may be further structuredto determine the incentive data 180814 based at least in part on theurgency data 180906. In certain embodiments, the incentivizer circuit180806 may be further structured to adjust the incentive data 180814after a first period of time 180910 to increase a value of an incentive180912 corresponding to the incentive data 180814. In certainembodiments, the incentivizer circuit may be further structured todetermine that the shift 180812 cannot be voluntarily fulfilled after asecond period of time 180914.

Referring to FIG. 17 , embodiments of the current disclosure provide fora method 181000 for incentive-based scheduling. The method 181000 may beperformed via apparatus 180800 and/or any other computing devicedisclosed herein. The method 181000 includes interpreting, via aschedule interpretation circuit, schedule data 181002, analyzing, via ashift analysis circuit, the schedule data 181004, identifying, via theshift analysis circuit, a shift 181006, assigning, via the shiftanalysis circuit, an employee value to the shift 181008, determining,via an incentivizer circuit, incentive data for the shift based at leastin part on the employee value 181010, and transmitting, via an incentiveprovisioning circuit, the incentive data 181012.

Referring to FIG. 18 , certain further aspects of the method 181000 aredescribed following, any one or more of which may be present in certainembodiments. In certain embodiments, the method 181000 may furtherinclude assigning an employer value to the shift 181102, and determiningthe incentive data based at least in part on the employer value 181104.In certain embodiments, the method 181000 may further includedetermining urgency data of the shift based at least in part byanalyzing the schedule data 181106, and determining the incentive databased at least in part on the urgency data 181108. In certainembodiments, the method 181000 may further include interpreting urgencydata 181110, and determining the incentive data based at least in parton the urgency data 181112. In certain embodiments, the method 181000may further include adjusting the incentive data after a first period oftime to increase a value of an incentive corresponding to the incentivedata 181114. In certain embodiments, the method 181000 may furtherinclude determining that the shift cannot be voluntarily fulfilled aftera second period of time 181116.

Referring to FIG. 19 , an agglomerate network 181200 for generatingschedule data 181202 may be provided. The apparatus 181200 includes ascheduler circuit 181204, a connector circuit 181206, and an incentivizeanalysis circuit 181208. The scheduler circuit 181204 is structured tooutput the schedule data 181202. The connector circuit 181206 isstructured to adjust at least one of an input 181210 to the schedulercircuit 181204 or the schedule data 181202 outputted by the schedulercircuit 181204, as disclosed herein. The incentivize analysis circuit181208 is structured to receive the schedule data 181202 via theconnector circuit 181206, identify a shift 181212 in the schedule data181202, assign an employee value 181214 to the shift 181212, determineincentive data 181216 for the shift 181212 based at least in part on theemployee value 181214, and transmit 181218 the incentive data 181216. Inembodiments, the connector circuit 181206 may be structured to adjustthe input 181210 to the scheduler circuit 181204 and/or the scheduledata 181202 outputted by the scheduler circuit 181204 in the event theincentivize analysis circuit 181208 determines that a particular shiftcannot be filled despite being assigned a high value inventive. In otherwords, the connector circuit 181206 may trigger a change to the scheduledata 181202 when the scheduler circuit 181204 generates a schedule thatcontains shift that no employee wants to fill. As will be understood,the changes to the schedule data 181202 may result in the offendingshift being removed and/or adjusted to make it more palatable/appealingto employees.

Referring to FIG. 20 , certain further aspects of the agglomeratenetwork 181200 are described following, any one or more of which may bepresent in certain embodiments. In certain embodiments, the incentivizeanalysis circuit 181208 may be further structured to assign an employervalue 181302 to the shift 181212, and the determination of the incentivedata 181216 may be further based at least in part on the employer value181302. In certain embodiments, the agglomerate network 181200 mayfurther include an urgency analysis circuit 181304 structured todetermine urgency data 181306 of the shift 181212 based at least in partby analyzing the schedule data 181202. The incentivize analysis circuit181208 may be further structured to determine the incentive data 181216based at least in part on the urgency data 181306. In certainembodiments, the agglomerate network 181200 may further include anurgency interpretation circuit 181308 structured to interpret urgencydata 181306. The incentivize analysis circuit 181208 may be furtherstructured to determine the incentive data 181216 based at least in parton the urgency data. In certain embodiments, the incentivize analysiscircuit 181208 may be further structured to adjust the incentive data181216 after a first period of time 181310 to increase a value of anincentive 181312 corresponding to the incentive data 181216. In certainembodiments, the incentivize analysis circuit 181208 may be furtherstructured to determine that the shift 181212 cannot be voluntarilyfulfilled after a second period of time 181314.

Referring to FIG. 21 , an apparatus 181400 may be provided. Theapparatus 181400 includes a schedule interpretation circuit 181402, ashift analysis circuit 181404, an incentivizer circuit 181406, and anincentive provisioning circuit 181408. The schedule interpretationcircuit 181402 is structured to interpret schedule data 181410. Theshift analysis circuit is structured to analyze the schedule data181410, identify a portion 181412 of the schedule data 181410, andassign an employee value 181418 to the portion 181412 of the scheduledata 181410. The incentivizer circuit 181406 is structured to determineincentive data 181414 for the portion 181412 of the schedule data 181410based at least in part on the employee value 181418. The incentiveprovisioning circuit 181408 is structured to transmit 181416 theincentive data 181414.

Referring to FIG. 22 , certain further aspects of the apparatus 181400are described following, any one or more of which may be present incertain embodiments. In certain embodiments, the incentivizer circuit181406 may be further structured to assign an employer value 181502 tothe portion 181412 of the schedule data 181410, and the determination ofthe incentive data 181414 may be further based at least in part on theemployer value 181502. In certain embodiments, the apparatus 181400 mayfurther include an urgency analysis circuit 181504 structured todetermine urgency data 181506 of the portion 181412 of the schedule data181410 based at least in part by analyzing the schedule data 181410. Inembodiments, determining the urgency data 181506 may be based at leastin part on user defined inputs, e.g., a user specifies that the portion181412 is of high value and/or of urgent need. In embodiments,determining the urgency data 181506 may be based at least in part on anartificial intelligence module/circuit that analyzes the schedule data181410 for choke points, e.g., points of a production flow that canprevent and/or greatly slow down the filling of an order, and assignchoke points that have processes that are close to not having enoughtime to complete a high urgency.

The incentivizer circuit may be further structured to determine theincentive data 181414 based at least in part on the urgency data 181506.In certain embodiments, the apparatus 181400 may further include anurgency interpretation circuit 181508 structured to interpret urgencydata 181506. The urgency data 181508 may be in the form of a score,e.g., one (1) to one-hundred (100) and/or have a labeled valuecorresponding to “urgent”. The incentivizer circuit 181406 may befurther structured to determine the incentive data 181414 based at leastin part on the urgency data 181506. In certain embodiments, theincentivizer circuit 181406 may be further structured to adjust theincentive data 181414 after a first period of time 181510 to increase avalue of an incentive 181512 corresponding to the incentive data 181414.In certain embodiments, the incentivizer circuit may be furtherstructured to determine that the portion 181412 of the schedule data181410 cannot be voluntarily fulfilled after a second period of time181514.

Referring to FIG. 23 , a method 181600 for inventive-based scheduling isprovided. The method may be performed via apparatus 181400 and/or anyother computing device disclosed herein. The method 181600 includesinterpreting, via a schedule interpretation circuit, schedule data181602, analyzing, via a shift analysis circuit, the schedule data181604, identifying, via the shift analysis circuit, a portion of theschedule data 181606, assigning, via the shift analysis circuit, anemployee value to the shift 181608, determining, via an incentivizercircuit, incentive data for the shift based at least in part on theemployee value 181610, and transmitting, via an incentive provisioningcircuit, the incentive data 181612.

Referring to FIG. 24 , certain further aspects of the method 181600 aredescribed following, any one or more of which may be present in certainembodiments. In certain embodiments, the method 181600 may furtherinclude assigning an employer value to the shift 181702, and determiningthe incentive data based at least in part on the employer value 181704.In certain embodiments, the method 181600 may further includedetermining urgency data of the shift based at least in part byanalyzing the schedule data 181706, and determining the incentive databased at least in part on the urgency data 181708. In certainembodiments, the method 181600 may further include interpreting urgencydata 181710, and determining the incentive data based at least in parton the urgency data 181712. In certain embodiments, the method 181600may further include adjusting the incentive data after a first period oftime to increase a value of an incentive corresponding to the incentivedata 181714. In certain embodiments, the method 181600 may furtherinclude determining that the shift cannot be voluntarily fulfilled aftera second period of time 181716.

Referring to FIG. 25 , a non-transitory computer-readable medium 181800for inventive-based scheduling is provided. The non-transitorycomputer-readable medium 181800 stores instructions that adapt at leastone processor to: interpret schedule data 181802, analyze the scheduledata 181804, identify, based at least in part on the analysis of theschedule data, a shift 181806, determine incentive data for the shift181808, and transmit the incentive data 181810.

Referring to FIG. 26 , certain further aspects of the non-transitorycomputer-readable medium 181800 are described following, any one or moreof which may be present in certain embodiments. In certain embodiments,the non-transitory computer-readable medium 181800 may further includeinstructions that adapt the at least one processor to assign an employervalue to the shift 181902, and determine the incentive data based atleast in part on the employer value 181904.

In certain embodiments, the non-transitory computer-readable medium181800 may further include instructions that adapt the at least oneprocessor to compare an employee value to the employer value 181906, anddetermine the incentive data based at least in part on a differencebetween the employee value and the employer value 181908. In certainembodiments, the incentive data may correspond to one of a plurality ofpossible incentives, each corresponding to a distinct incentive valuethat shares a common scale with the employee value and the employervalue, such that the plurality of possible incentives has an increasingvalue ordering.

The systems and methods described herein for incentivized schedulingprovide various technical benefits and improvements over known methods.In one aspect, the systems and methods provide for efficient utilizationof computing resources. In one aspect, the system and methods enableefficient utilization of resources by adapting computation resources tourgency interpretations. Systems and methods described herein enableadaptation of models based on the urgency associated with schedules. Inone example, models may identify urgent needs and adapt schedulegeneration to generate higher confidence schedules using incentives. Inanother aspect, the use of incentive modeling allows a tradeoff betweencomputation time and schedule incentives to find adequate schedules. Themethods allow adaptive incentive inclusion to reduce computation load.In some cases, incentive inclusion may be dynamically configured duringtimes of peak resource load thereby reducing computation requirementswhen computation resources may be constrained. In one example, highconfidence schedules may be identified with less computation time byincluding more and/or higher value incentives compared to generationwithout incentives.

The integrated recruiting tool may suggest the job to the employeeand/or the recruiting tool could suggest an employee trade/contractbetween companies. As such, embodiments of the current disclosure mayprovide for an artificial intelligence, which may form part ofcomponent/module 116 (FIG. 1 ), that suggests employees for sharing,trades, and/or contracts between companies with regard to scheduling.Embodiments of the current disclosure may also determine “team” tradesbetween companies, e.g., trades that involve a group of employees on atleast one side of the trade. Non-limiting examples of such trades and/orcontracts may be for a shift, day, week, weeks, month, months, year,years, and the like. Embodiments of the current disclosure may determinepotential employees and/or teams (of employees) for sharing betweendifferent employers based in part on having access to human recoursescheduling data, which may be from multiple entities/corporations.Embodiments of the current disclosure may determine potential employeesand/or teams for sharing between different employers via determiningaspects such as skills and/or constraints on the employees, teams,and/or employers. Embodiments of the current disclosure may provide foremployees to indicate their willingness and/or availability to beshared. Embodiments of the current disclosure may also provide foremployers to identify employees for sharing, and/or may operate inconjunction with an inventive-based scheduler, such as those describedherein, e.g., apparatus 180100 (FIG. 8 ). Embodiments may be integratedwith a human resources tool and/or platform, such as to make asuggestion to either the employee or the employee's employer(s) aboutsharing possibilities.

Accordingly, referring to FIG. 27 , an apparatus 270100 for employeecontracting/sharing is provided. The apparatus 270100 includes a firstschedule interpretation circuit 270102 structured to interpret 270112first schedule data 270122 for an employee of a first entity 270120; anavailability determination circuit 270104 structured to determine 270114availability data 270124 for the employee based at least in part on thefirst schedule data; and a second schedule interpretation circuit 270106structured to interpret 270116 second schedule data 270130 correspondingto a second entity 270128. The apparatus 270100 further includes: asharing circuit 270108 structured to determine 270118, based at least inpart on the availability data and the second schedule data, that theemployee is available to work a shift 270126 corresponding to the secondentity; and a shared employee provisioning circuit 270110 structured totransmit 270132 an indication 270134 that the employee is available towork for the shift. Non-limiting examples of first schedule data 270122and second schedule data 270130 include data corresponding to schedulesand/or data related to the schedules, e.g., a total number of hoursworked, a total number of workers per shift, an estimated amount ofsales, an estimated amount of profits, a location, a predicted commutetime, etc. Non-limiting examples of availability data 270124 includeindications of a time block for which the corresponding employee is notassigned a shift; indications that a shift may be overstaffed andtherefore an employee associated with the shift may not be necessary forthe shift and, thus, available for sharing; indications that a piece ofequipment required to complete a shift is out of commission and that theemployee may be of more value if completing another task; an indicationthat a weather related event may cause a location associated with ashift to be delayed in opening and/or closed, thus, indicating theemployee may be of more value if completing another task; and the like.

Certain further aspects of the apparatus 270100 are described following,any one or more of which may be present in certain embodiments. Forexample, the first 270120 and the second 270128 entities may be distinctdepartments within a same organization. In embodiments, the first 270120and the second 270128 entities may be distinct organizations, e.g.,different corporations.

Embodiments of the apparatus 270100 may also form part of an agglomeratenetwork, e.g., 200 (FIG. 2 ). For example, the apparatus 270100 may beone of a plurality of agglomerate network circuits connected viaconnector circuits, as disclosed herein. In such embodiments, theapparatus 270100 may be a schedule analysis circuit, or another type ofagglomerate network circuit, as described herein, that identifiesopportunities for sharing employees wherein the apparatus 270100 maydirectly adjust schedule data flowing through the agglomerate networkand/or adjust one or more connectors so as to indirectly adjust theschedule data flowing through the agglomerate network. For example, theapparatus 270100 may identify a sharing opportunity and then adjust aconnector so that a schedule corresponding to the schedule datasatisfies/fills and/or otherwise incorporates the sharing opportunity.In embodiments, both entities involved in a sharing opportunity, e.g.,the one that has an available employee and the one that needs anemployee to fill a shift, may have their schedules generated by theagglomerate network. In other words, embodiments of an agglomeratenetwork that incorporate employee contracting/sharing, as disclosedherein, may generate schedules for two or more entities. In embodiments,an agglomerate network may concurrently generate two or more scheduleseach respectively corresponding to one of two or more entities, wherethe agglomerate network tries to optimize the needs of the entities bytrying to maximize the use of available employees. In embodiments, theagglomerate network may cycle through one or more generations ofschedule data in order to reach a satisfactory level of optimization,e.g., a 30% increase in filled shifts as opposed to schedules that donot incorporate employee sharing/contracting, as disclosed herein.

Referring to FIG. 28 , in embodiments, the apparatus 270100 may furtherinclude a contract generation circuit 270202 structured to generate270212 a contract 270222 between the first entity 270120 and the second270128 entity that obligates the employee to work the shift. Inembodiments, the indication 270134 (FIG. 27 ) transmitted by the sharedemployee provisioning circuit 270110 includes the contract 270222. Thecontract 270222 may be a traditional contract, e.g., a static document,or smart contract that may be based at least in part on a blockchain.For example, in embodiments, acceptance of the contract 270222 may befacilitated by a hashed digital signature that is added to theblockchain. In embodiments, the indication 270134 may include a link tothe contract 270222.

In embodiments, determining the availability of the employee via thesharing circuit 270108 may be based at least in part on exchanging theemployee for another employee from the second entity, wherein theemployee from the second entity works a shift intended for the employeefrom the first entity. In other words, the employees may be swapped. Forexample, company A may require a job B to be completed where job Brequires skill set X, and company D may require a job E to be completedwhere job E requires skill set Y. Further, company A may have employee Rwho has skill set Y and company D may have employee S who has skill setX. Embodiments of the current disclosure may detect this job/skillmismatch and arrange for company A and D to exchange employees R and Sso that the jobs B and E can be completed. As will be understood, suchan arrangement mutually benefits both company A and company D.

In embodiments, the indication 270134 may provide for an employee toprovide feedback 270226 regarding an opportunity to work a shift (foranother entity). The feedback 270226 may include an option 270228 forthe employee to agree or refuse the opportunity. The indication 270134may provide for an entity to provide feedback regarding the opportunityto share its employee. The feedback may include an option 270228 toapprove or refuse the opportunity. The indication 270134 may includeoptions 270228 for both an entity and an employee to approve or refusethe sharing opportunity. In embodiments, approval 270230 from both theemployee and the entity may be required for the employee to work a shift(at the second entity).

As will be understood, the contract 270222 may specify one or morepayment arrangements with respect to the sharing of employee(s). Forexample, in embodiments, the second entity 270128 may compensate theemployee for working the shift. In embodiments, the second entity 270128may compensate the first entity 270120 (the one that employee isoriginally associated with) for the employee working the shift. Inembodiments, the second entity 270128 may compensate the first entity270120 and the employee for working the shift.

In embodiments one or more additional employees of the first entity270120 may be determined as being available to work the shift with thefirst employee. For example, the one or more additional employees andthe first employee may be on a same team in the first entity 270120. Inembodiments, the one or more additional employees may be in a samedepartment of the first entity 270120.

In embodiments, the apparatus 270100 may further include a constraintidentification circuit 270210 structured to determine 270220 constraintdata 270234 corresponding to the employee. Non-limiting examples ofconstraint data 270234 include: maximum number of hours available towork in a day, week, month, year, etc.; physical limitations, excludedtime periods, e.g., no morning shifts, no evening shifts, etc.; amaximum amount of pay per day, year, month, year, etc.; crew restrequirements; employee(s) that must be co-workers; employee(s) that mustbe avoided, etc. In such embodiments, determination 270114 theavailability data 270124 may be further based at least in part on theconstraint data 270234.

In embodiments, the constraint data 270234 may be determined byanalyzing the schedule data 270122 and/or 270130 (FIG. 27 ). Forexample, the constraint identification circuit 270210 (FIG. 28 ) maydetermine that a particular employee has already worked thirty-five (35)hours for a given week and therefore is only available to work anadditional five (5) hours in order to avoid having to pay the employeeovertime.

As disclosed herein, the constraint data 270234 may be based at least inpart on human resource data 270232 retrieved from a database. Thedatabase may be associated with the first entity 270120 and/or thesecond entity 270128. For example, embodiments of the current disclosuremay retrieve information from the database such as, but not limited to:a number of hours worked by an employee; a skill set possessed by anemployee; a performance evaluation (which may be in the form of a score,e.g., “high performer”, “satisfactory performer”, and/or “needsimprovement”); physical limitations, e.g., a lifting weight limit;and/or the like. Further non-limiting examples of constraint data 270234include data corresponding to a minimum wage per hour, a minimum amountof pay, an amount of overtime, and the like.

In embodiments, the apparatus 270100 includes a plurality of agglomeratenetwork circuits 270204 connected via a plurality of connector circuits270206, as disclosed herein, that generate 270214 the first scheduledata 270122 (FIG. 27 ). In embodiments, a connector circuit of theplurality 270206 biases 270224 the first schedules data 270122, asdisclosed herein. As such, the bias 270224 may weight the first scheduledata 270122. In embodiments, an agglomerate network circuit of theplurality 270204 may be structured to incorporate weather data into thefirst schedule data 270122. The plurality of agglomerate networkcircuits 270204 may include a schedule warden circuit, e.g., 270208and/or 20100 (FIG. 37 ), as disclosed herein, that verifies 270216 thatthe sharing of an employee, as disclosed herein, will not violate aschedule norm, e.g., one relating to a maximum amount of hours workedper week.

Referring to FIG. 29 , a method 270300 for employee contracting/sharingis shown, in accordance with embodiments of the current disclosure. Themethod may be performed via apparatus 270100 and/or any other computingdevice disclosed herein. The method 270300 includes interpreting, via afirst schedule interpretation circuit, first schedule data for anemployee of a first entity 270302; and determining, via an availabilitydetermination circuit, availability data for the employee based at leastin part on the first schedule data 270304. The method 270300 furtherincludes interpreting, via a second schedule interpretation circuit,second schedule data corresponding to a second entity 270306; anddetermining, via a sharing circuit and based at least in part on theavailability data and the second schedule data, that the employee isavailable to work a shift corresponding to the second entity 270308. Themethod 270300 further includes transmitting, via a shared employeeprovisioning circuit, an indication that the employee is available towork for the shift 270310.

Referring to FIG. 2704 , certain further aspects of the method 270300are described following, any one or more of which may be present incertain embodiments. For example, the first and the second entities maybe distinct departments within a same organization. In embodiments, thefirst and the second entities may be distinct organizations. Inembodiments, the method 270300 may further include generating a contract270422 between the first entity and the second entity 270412, where thecontract 270422 may obligate the employee to work the shift. Asdisclosed herein, the transmitted indication 270134 (FIG. 27 ) mayinclude the contract 270422, and/or the contract 270422 may be a smartcontract based at least in part on a blockchain. Acceptance of thecontract 270422, e.g., a digital signature, may be hashed and added tothe blockchain.

In embodiments, determining the availability data for the employee270304 (FIG. 29 ) may be based at least in part on exchanging a firstemployee (from the first entity) for a second employee (from the secondentity), where the second employee works a shift intended for the firstemployee.

In embodiments, the indication 270134 (FIG. 27 ) provides for theemployee to provide feedback 270426 (FIG. 30 ) regarding the opportunityto work the shift. The feedback 270426 may include an option 270428 toagree or refuse the opportunity. The indication 270134 may also providefor the entity to provide feedback regarding the opportunity to sharethe employee. In such embodiments, the feedback 270426 may include anoption 270428 to approve and/or refuse the opportunity. The indication270134 may include options 270428 for both an entity and an employee toapprove or refuse the sharing opportunity. Approval 270430 (FIG. 30 )from both the employee and the entity may be required for the employeeto work the shift.

In embodiments, the method 270300 may further include determining aconstraint data 270434 corresponding to the employee 270420. In suchembodiments, determining the availability data 270304 (FIG. 29 ) may befurther based at least in part on the constraint data 270434. Inembodiments, the constraint data 270424 may be determined 270420 (FIG.30 ) by analyzing the schedule data 270122 (FIG. 27 ). The constraintdata 270122 may be based at least in part on human resource data 270432(FIG. 30 ) retrieved from a database. The constraint data 270434 maycorrespond to a maximum number of hours worked per period of timeincluding at least one of a day, a week, a month, or a year. Theconstraint data 270434 may correspond to a minimum wage per hour, aminimum amount of pay, an amount of overtime, and the like.

In embodiments, the method 270300 may further include generating thefirst schedule data 270414 and biasing the first schedule data 270424,where the bias may weight the first schedule data. In embodiments, themethod 270300 may further include incorporating weather data into thefirst schedule data 270415 and/or verifying that sharing of the employeewill not violate a schedule norm 270416, e.g., a norm relating to anamount of hours worked. In embodiments, the employee may be legallyemployed by the first entity, may be a volunteer, may be a contractor,and the like.

Referring to FIG. 31 , a non-transitory computer-readable medium 270500,in accordance with embodiments of the current disclosure, is provided.The non-transitory computer-readable medium 270500 includes instructionsthat adapt at least one processor to: interpret first schedule data foran employee of a first entity 270502; determine availability data forthe employee based at least in part on the first schedule data 270504;and interpret second schedule data corresponding to a second entity270506. The instructions further adapt the at least one processor todetermine, based at least in part on the availability data and thesecond schedule data, that the employee is available to work a shiftcorresponding to the second entity 270508; and transmit, an indicationthat the employee is available to work for the shift 270510.

Referring to FIG. 32 , certain further aspects of the non-transitorycomputer-readable medium 270500 are described following, any one or moreof which may be present in certain embodiments. For example, inembodiments, the stored instructions further adapt the at least oneprocessor to: generate a contract 270622 between the first entity andthe second entity that obligates the employee to work the shift 270612.In embodiments, the indication 270134 (FIG. 27 ) transmitted by theshared employee provisioning circuit includes the contract 270622. Asalso disclosed herein, the contract 270622 may be a smart contract thatis based at least in part on a blockchain, and/or acceptance of thecontract 270622 may be hashed and added to the blockchain.

Referring to FIG. 33 , embodiments of the current disclosure may providefor an agglomerate network 270700 that provides for employeecontracting/sharing. As shown in FIG. 33 , the agglomerate network270700 includes a scheduler circuit 270702 structured to output 270708first schedule data 270724 corresponding to a first entity 270722. Theagglomerate network 270700 further includes a connector circuit 270704structured to adjust at least one of an input to the scheduler circuitor the first schedule data outputted by the scheduler circuit 270710.The agglomerate network 270700 further includes a shared employeecontracting circuit 270706 structured to: interpret 270712 the firstschedule data 270724; interpret 270714 second schedule data 270728corresponding to a second entity 270726; and determine a need of thesecond entity 270726 for a worker based at least in part on the secondschedule data 270716. The shared employee contracting circuit 270706 isfurther structured to generate 270718 a change command value 270730structured to trigger an adjustment to the connector circuit to effect achange of at least one of the input to the scheduler circuit or thefirst schedule data outputted by the scheduler circuit such that theemployee is made available to fill the need of the second entirety for aworker. The shared employee contracting circuit 270706 is furtherstructured to transmit 270720 the change command value.

Referring to FIG. 34 , certain further aspects of the agglomeratenetwork 270700 are described following, any one or more of which may bepresent in certain embodiments. For example, the shared employeecontracting circuit 270706 may further include a contract generationcircuit 270802 structured to generate 270812 a contract 270822 betweenthe first entity and the second entity. The contract 270822 may obligatethe employee to work the shift, and the indication transmitted by theshared employee provisioning circuit may include the contract. Asdisclosed herein, the contract 270822 may be a smart contract that isbased at least in part on a blockchain, and acceptance of the contract270822 may be hashed and added to the blockchain.

The systems and methods described herein for shared employee contractorprovide various technical benefits and improvements to processingschedules. In one aspect, the systems and methods provide for efficientmanagement of agreements and contracts as they relate to schedules. Inmany cases, different organizations may have separate inoperable systems(due to security, regulatory, or other concerns and constraints).Providing elements related to the schedule, such as contracts, on theblockchain allows different entities to effectively share and maintain asecure and verifiable shared data resource. The blockchain allowsdifferent entities to effectively share electronic information relatedto schedules even when the schedule systems of the entities may not beoperable or accessible with each other. The system and methods enableefficient utilization of resources by utilizing blockchains to maintainand track contracts as they relate to schedules.

In embodiments, the schedule flexor may be a turnover schedule flexorthat seeks to produce schedules and/or provide employee incentives tomitigate and/or eliminate employee turnover. For example, embodiments ofthe turnover schedule flexor may favor giving an employee aneasier/improved schedule when the risk of turnover for that employee ishigh but favor the employer, e.g., giving the employee a harderschedule, when the risk of turnover for that employee is low. Inembodiments, “favoring” by the system may be to affect short-termprofits. Accordingly, other non-limiting examples of where embodimentsof the system may favor the employee or the employer include:

-   -   A. Where there is a risk of the employee leaving, e.g., as        measured by sentiment analysis of an HR survey. For example, the        system may prioritize that employee's preferences for this        week's schedule even though the result causes overage for this        week, reducing sales margins. Another example may be a        deficiency for the week, resulting in additional labor being        brought in (again, reducing profit).    -   B. In a schedule marketplace, as described herein, under a        process that rewards employee acceptance of less desirable        shifts with shift premiums, the employee/employer benefits may        tend to balance each other out, assuming an efficient        marketplace. As will be understood, in practice, however, the        terms of shift premium rewards may be set by the employer, and        so the employer may not be completely fair.    -   C. In scenarios involving hidden long-term benefits learned by        embodiments of the system, as disclosed herein, that have        short-term negative impacts on profits, e.g., where the system        has learned that approving vacation helps the productivity of an        employee, even though the short-term result is a deficiency.

The turnover schedule flexor may also provide and/or suggest incentivese.g., additional pay, vacation time, points, etc. when an employee isgiven a difficult schedule and/or in situations where the employer hasan urgent need for the employee. Non-limiting examples of improvedschedules include less physically demanding jobs, preferred shifts,better paying shifts, promotions for star performers, etc.

In embodiments, the agglomerate models may couple/join as indicated inthe examples herein.

-   -   An event model might provide inputs to a schedule model        regarding the excess staffing requirements associated with an        upcoming event.    -   The schedule model in turn develops a schedule that meets the        staffing requirements associated with the given event.    -   The sentiment analysis model recognizes that the new schedule        requires substantial overtime and several closing/opening shifts        for key employees and updates employee sentiment accordingly.    -   An absenteeism model predicts that several employees will        call-in-sick or otherwise miss their assigned schedule times.    -   A retention model predicts that several employees may leave        given the current sentiment.    -   A capacity model updates it turnover rates, hiring predictions,        etc., and predicts available capacity at this franchise and        across a network of franchises.    -   A future scheduling activity cannot develop a satisfactory        schedule for a future event based on the modeled capacity.

In embodiments, the evolution controller determines when the models havecycled sufficiently.

Referring to FIG. 35 , the evolution controller 334 may communicate withthe Hierarchical Feature Propagator 332 and Model Connectors 510, 512 todetermine if additional model runs may be required in order to reducethe level of extrapolation required to generate corrected featureinputs.

In embodiments, if a confidence measure remains out of bounds, then theAutonomous Evolution Controller 334 may choose to rerun an earlier modelwith the most recent data (if a higher resolution model is available),or with a selection of probabilistic options, the system can carry forththrough to the next stage of processing, e.g., for the use case shown inFIG. 35 , options that would drive relative absenteeism rate (such asminimum actual snow fall, no school closing and the like) to optionsthat would drive a high absenteeism rate (such as more than theforecasted 6″ of snow, more school closings and the like). Themultiplicity of results could be carried through the next schedulegeneration states in order to better assess the staffing risks and fordetermining the best staff schedule.

In the embodiment of FIG. 35 , the correlated bias indicators 514 arefeatures that are not adequately considered within the raw model outputdata. To accommodate these variables, the trainable Adjust Raw ModelOutput performs an adjustment on the raw data, if possible. This mayalso impact confidence.

A use case in relation to FIG. 35 is described below. An absenteeismmodel may take as input a particular franchise location includingnumerous business values useful to predicting absenteeism (such assentiment, schedule, time of the year, etc.) but from past experience,the model does not adequately consider the impact of school closures orsignificant snowstorms. In this case, predicted snowfall rates alongwith their confidence are input to adjust absenteeism outputs based onschool closure information (Variable A), employees within affectedschool districts (Variable B), and the predicted snowfall rates(Variable C). The model, trained on similar data, adjusts the modeloutputs and confidence appropriately.

FIG. 36 depicts an agglomerate model information propagation process600, in accordance with embodiments of the current disclosure. Inembodiments, the hierarchical feature propagator 332 (FIG. 35 ) may beconfigured to act on data transfers between models and modeling timehorizons, propagate uncertainty through the network as probabilitydistributions, and improve sparsely sampled data from an individualsite, company, or time-period using cross-site, cross-company, andhistorical data. In embodiments, the autonomous evolution controller 334(FIG. 35 ) may be configured to determine when to execute a model updatebased on propagated data, determine when to explore probability space,(i.e., the execution of models to examine non-consensus results), anddetermine when to generate alternative or predictive solutions tosupport rapid editing, model evolution, and/or enable sensitivityanalysis.

Embodiments may include a semi-autonomous goal setter 336 (FIG. 35 ). Ata top-level, business goals may be defined in the context of maximizingsome combination of corporate metrics, e.g., profit, cash-flow, customerretention, employee retention, etc. In some cases, goals such asemployee retention, may be unstated or unmeasured as a specificobjective; its impact may only be felt as a contributor to a higher goalsuch as profit/loss. In embodiments, the semi-autonomous goal setter maybe to build an understanding of the high-level corporate goals (e.g.,profit/loss) and to translate such high-level goals, where possible, tointermediate goals such as employee retention, sentiment, frequency ofschedule editing, absenteeism, store coverage, local sales, etc. Thesemi-autonomous goal setter may examine data over time and acrosssimilar departments, stores, related industries, regionally, and/orwithin common macro-economic environments (unemployment rate, jobturnover, pay, recruiting environment, etc.), to set intermediate goalsthat are useful for the training of individual models, or a subset ofagglomerate models.

In embodiments, the Semi-Autonomous Goal Setter may continuously, orperiodically, monitor high level goals and decrease its confidence in anintermediate goal(s) if achieving the intermediate goals results inhigher level goals performing better/worse than might be imputed fromthe intermediate values. When the anticipated correlation falls below adefined or learned threshold, the Semi-Autonomous Goal Setter mayrecalculate one or more intermediate model goals.

Embodiments may include a continuous model validator. In embodiments, anelement may be included that identifies when intermediate or final modeloutputs are insufficiently predictive of the actual behavior of thesystem, thus generating a need for a new and/or improved HierarchicalFeature Propagator (for instance, a new bias connector, or modifiedweights on a hierarchical mixer) or Autonomous Evolution Controller (forexample, an altered threshold for playing multiple options through thesystem).

Embodiments of the system, as disclosed herein, may also provide for aschedule warden that uses artificial intelligence (AI) and/or machinelearning (ML) to monitor a schedule to detect when scheduling conditionsfall outside of company norms. Embodiments of the schedule warden mayform part of component/module 128 (FIG. 1 ). The schedule warden mayinclude automatic adjustments or recommendations. The schedule wardenmay use “normal” baselines defined by a user or determined by the AIand/or ML looking at past data. The schedule warden may be structured todetect one or more of: preferential overtime for certain employees;preferential shifts for certain employees; lack of fairness for time offrequests; and/or unfair scheduling editing. The schedule warden maycompare schedule data across all relevant locations, e.g., locations ofa franchise, for norms, or across industry norms, e.g., all fast-foodstores.

Embodiments of the schedule warden may provide for the use of artificialintelligence (AI) to monitor/inspect a generated schedule to detect ifscheduling conditions/properties of the generated schedule fall outsideof scheduling norms. In embodiments, “normal”, as used with respect toschedule data, includes conditions where schedule properties of aschedule being evaluated align with prior schedules used by an entity,and/or schedule properties of the schedule being evaluated conform toindustry customs and/or legal requirements. In embodiments, “normal”, asused with respect to schedule data, may be defined by a user and/ordetermined by AI looking at past data. Non-limiting factors fordetermining “normal” may include fairness, past schedules, industryaverages, etc. Non-limiting factors for determining “normal” may includedetermining statistics for historical data and identifying thresholdsfor elements which may be classified as normal.

Embodiments of the current disclosure may also provide for automaticschedule adjustments and/or recommendations in response to detectingthat scheduling conditions fall outside scheduling norms. A non-limitinguse case may include detecting situations where a supervisor isconsistently giving another employee an exceptionally easy/favorableschedule and/or another employee an exceptionally hard/difficultschedule. Another non-limiting use case may include detecting when asupervisor is generating schedules that push the work-life balance toofar in one direction. Embodiments may use scoring scale(s) to comparefairness and/or work-life balance. Scales may be dynamic based onseasonality, location, life events, etc.

Accordingly, referring to FIG. 37 , an apparatus 20100 for determiningwhen scheduling conditions fall outside of an entity's normal schedulingpractice is depicted. The apparatus 20100 may include a scheduleinterpretation circuit 20102 structured to interpret schedule data20104, a warden circuit 20108 structured to determine, based at least inpart on the schedule data 20124, that a property of the schedule data (aschedule property 20134) violates a schedule norm 20110 and generates anorm violation parameter 20132. The apparatus 20100 may further includea corrective action circuit 20112 structured to generate, in response tothe determination that the property violates the schedule norm and/orthe norm violation parameter 20132, a corrective action command value20114 to be transmitted to a corrective action provisioning circuit20118. The corrective action command value 20114 may be structured totrigger an adjustment to the schedule data 20104 to effect a change inone more schedule properties 20134 such that the schedule data 20104 nolonger violates the schedule norm 20110. In embodiments, the adjustmentmay be a direct change to the schedule data 20104, e.g., an addition ofa shift, a swapping of assigned workers to a shift, a removal of ashift, an extension of a shift, a shortening of a shift, and/or anyother change to a property of a schedule and/or related aspects thereof.

In embodiments, the apparatus 20100 may include a historic scheduleinterpretation circuit 20128 structured to interpret historic scheduledata 20124 and a norm detection circuit 20120. The norm detectioncircuit 20120 may be structured to generate a historic schedule trendvalue 20122 based, at least in part on the historic schedule data 20124.In some embodiments, a neural network 20130 may identify the historyschedule trend value 20122. The neural network 20130 may be part of thenorm detection circuit 20120, the historic schedule interpretationcircuit, 20128, or located in an external device. The norm detectioncircuit 20120 may be further structured to generate the schedule norm20110 based, at least in part on, the historic schedule trend value20122, and/or historic schedule data 20124. The historic schedule data20124 may include data for the same entity as the schedule data 20104and/or for a different entity, where the different entity may be in asimilar industry, a geographical location with similar employmentregulations and customs, for a similar manufacturing line or process,and the like. The neural network 20130 may be based, at least in part,on unsupervised learning using historic schedule data 20124 and/or ahistorical trend value 20122. The neural network 20130 may be furthertrained and/or updated as new data becomes available.

The warden circuit 20108, or a subsidiary score generation circuit20402, may generate a plurality of schedule scores 20138 for theschedule data 20104 where the schedule scores 20138 may be relative toone or more schedule properties 20134, combinations of scheduleproperties, statistics of one or more schedule properties, statisticalrelationships between schedule properties, and the like. For example, aschedule that has an average shift length of six hours with acorresponding wage that is above the industry average may get a higherschedule score 20138 with respect to employee retention than a schedulethat has an average shift length of nine hours with a corresponding wagethat is below the industry average.

Referring to FIG. 38 , schedule properties 20134 may include an eventproperty 20304, an employee property 20310, a shift favorability score20308, a cost property 20312, a seasonal property 20314, a personnelproperty 20318, a location property 20320, equipment property 20322, andthe like. An event property 20304 may be indicative of a disruptiveevent such as an expected maintenance shutdown, a failure of equipment,a weather-related shutdown, a significant increase in productiontargets, and the like. A shift favorability score 20308 may beindicative as to how desirable a shift may be. This may be influenced bytime of day (A, B, or C shift), type of shift (swing shift, 10 on/4 off,and the like), shift days or dates (weekends, holidays, and summers),work being done on the shift, availability of overtime pay, groupinteractions, and the like. Shift favorability score may be for a shiftoverall or may vary with employee and their preferences.

An employee property 20310 may include employee status (e.g., regular,contractor, on probation), a seniority, a rating, an hourly rate, askill set, a certification, a clearance level, a limitation that mightaffect schedule and/or working conditions, any needed adaptations,employee interactions, and/or the like. For example, if work is slow andshifts are limited, preference may be given to an employee over acontractor. Skill sets, certifications, clearance level and the like mayaffect whether an employee is qualified to work a particular shiftand/or a particular station/job during a shift. Limitations and neededadaptations may impact total working hours, types of shifts the employeemay be assigned, the locations where an employee may be assigned and thelike. An employee property 20310 may be cumulative over a period of timesuch as per day, per week, per month, per pay period, per quarter, peryear, over career, etc.; either from a historic perspective or to datewithin a current time period selected (e.g., month to date, week todate, year to date, and the like) with or without the inclusion of thecurrent schedule data. In embodiments, employee property 20310 mayinclude cumulative hours worked, cumulative overtime hours, cumulativeshift favorability (i.e., number of preferred shifts for an employee orgroup of employees compared to unfavorable shifts) where shiftfactorability may be historical or related to the specific scheduledata. Employee interactions may include properties that are linked withspecific employees or groups of employees such as interaction ratingsbetween employees, including managers, who would be working together.Interaction ratings may indicate how well employee combinations getalong, whether they work well together, duplication of skill sets,roles, and the like. Interaction ratings may be based at least on ascore, range, scale, or the like.

A cost property 20312 may include projections of the cost of the shiftas configured (in terms of personnel costs, equipment costs, materialscosts, and the like), anticipated output, return on shift, and the like.

A seasonal property 20314 may include season of year, weatherconditions, and the like. For example, reduced shift hours may beacceptable for workers exposed to the elements if weather conditions aresubstantially out of the norm, such as during a heat wave.

A personnel property 20318 may identify who and/or how many people areavailable to fill a shift. This may account for scheduled vacations,overall staffing, and the like.

Location property 20320 may include information regarding geographicallocation of shift (e.g., which plant), building location, manufacturingline, workstation, and the like. Some entities may have multiplelocations in a community and/or compound/campus with multiple buildingsat different locations and the like.

Equipment property 20322 may include information regarding whichequipment will be used, any specific skills required for use of theequipment, condition of the equipment (e.g., operating reduced capacity,pending maintenance, etc.), cycle times of the equipment, etc.

Referring to FIG. 39 , in some embodiments, a warden circuit 20108 mayfurther include a subsidiary score generation circuit 20402 forgenerating a plurality of schedule scores 20138 with respect to aschedule property 20134, a combination of schedule properties 20134, andthe like. Generation of a schedule score 20138 may include a scoringscale 20404 which may be reflective of various schedule properties20134. Determination of a schedule score 20138 may be based, at least inpart, on a distance of a property value or schedule data from acorresponding baseline value. In some embodiments, the scoring scale20404 may be dynamic such that the score for a given distance may beadjusted based on various schedule data.

For example, a disruptive event property 20304 such as an equipmentfailure resulting in a partial or full shutdown, a weather-relatedshutdown, or the like, may result in a dynamic scoring scale 20404adjusted to allow for long working hours to accommodate catching up onand/or meeting production targets. For example, a life event identifiedin an employee property 20310, such as a medical issue, school, and thelike, may result in a dynamic scale accommodating a wider distributionbetween that employee's hours and those of other employees. The dynamicscoring scale 20404 may be responsive to change in available personnel(a personnel property 20318), such as a change in available personnel.An increase in personnel would suggest that more employees should bescheduled for shorter shifts, resulting in a more stringent with respectto long working hours, while a decrease in personnel count would suggestthat employees might asked to be work longer hours or more shifts tocover for the missing personnel. This may result in the dynamic scoringscale 20404 becoming less stringent with respect to longer workinghours.

In determining that a schedule property 20134 violates a schedule norm20110, the warden circuit 20108 may retrieve one or more baselineschedule values 20408 corresponding to schedule properties 20134, thesebaseline schedule values 20408 may include a baseline for a scheduleproperty or a baseline for a score associated with that scheduleproperty. A combination of baseline schedule values 20408, scheduleproperties 20134, and schedule scores 20138, may be used to determinethat the schedule data 20104 is out of alignment with the baselineschedule values 20408. In embodiments, the one or more baseline schedulevalues 20408 may be retrieved from a database, other data source, and/orentered/provided by a user.

An example of a schedule property 20134 violating a schedule norm 20110may be related to: one or more employees having an unusually high, orlow, number of hours worked relative to the schedule norm; one or moreemployees having an abnormal amount of overtime pay relative to acorresponding schedule norm; one or more employees having an abnormalnumber of favorable or unfavorable shifts, or unusually high or lowshift favorability relative to a corresponding schedule norm; a pairingof employees on a shift who are known to conflict with each other, apairing of employees on a shift who otherwise should not be pairedtogether, e.g., a pair of employees who may be subject to aninvestigation, etc.

Referring to FIG. 40 , a method 20200 for determining when schedulingconditions fall outside of an entity's normal scheduling practice isdepicted. The method may include interpreting schedule data 20202 anddetermining whether a property of the schedule data violates a schedulenorm 20204 where the schedule norm may be based, at least in part, onhistorical schedule data. If there is a violation, the method 20200 mayinclude generating a corrective action command value 20208, in responseto a determination that a property of the schedule data does violate aschedule norm. The method 20200 further includes transmitting thecorrect action command value 20210 and adjusting, as a result of thecorrective action command value, a schedule property 20212, such thatthe corresponding schedule data no longer violates schedule norms. Inembodiments, a non-transitory computer-readable medium storinginstructions may cause a processor to perform the method 20200. Inembodiments, a schedule interpretation circuit may interpret scheduledata and a warden circuit may make the determination regarding anyviolations of schedule norms. A corrective action circuit may generate acorrective action command value to be transmitted by a corrective actionprovisioning circuit.

Referring to FIG. 41 , a method 20500 for adjusting a schedule to assureequitable schedules is depicted. The method 20500 may includetransmitting 20502 historical schedule data, via a local computingdevice, to a scheduling platform hosted on one or more remote servers.The method 20500 may further include accessing 20504, via the localcomputing device, schedule data generated via the scheduling platformand conforming 20508 the generated schedule data to schedule normsdetermined from the historical schedule data. In embodiments conforming20508 schedule data to schedule norms includes adjusting the scheduledirectly, e.g., directly manipulating the schedule data in memory,and/or adjusting the schedule data via connectors, as disclosed herein,so as to prevent and/or mitigate a schedule corresponding to theschedule data from violating schedule norms, e.g., legal standards,entity norms, industry norms, and/or other types of regulations and/orcustoms associated with the schedule data. The method 20500 may furtherinclude executing 20510 a portion of a schedule based on the scheduledata. In embodiments, the executed portions may include a portion of ashift, a full shift, a plurality of shifts, etc. A schedule wardencircuit, as disclosed herein, may be used to conform the draft scheduledata to schedule norms determined from historical schedule data asdescribed elsewhere herein resulting in adjustments to accessed data.The method 20500 may further include adjusting 20212, as a result of thecorrective action command value, a schedule property.

Referring to FIG. 42 , an agglomerate network 20600 for generatingschedule data is depicted. The agglomerate network 20600 includes ascheduler circuit 20602 to generate a draft schedule or a portion of adraft schedule based at least in part on schedule inputs 20610 andprovide corresponding output schedule data 20604. Connector circuit20608 may adjust one or more of the schedule inputs 20610 provided tothe scheduler circuit 20602 or the output schedule data 20604. Inembodiments, the schedule inputs 20610 may include a period of time, anumber and/or listing of available employees, a number and/or listing ofavailable equipment, data from a weather model, a listing of specialevents, e.g., holidays, annual sales, etc., and/or any other type ofdata for consideration in generating a schedule. A schedule wardencircuit 20612 may interpret the output schedule data 20604 anddetermine, based at least in part on the output schedule data 20604,that a property of the output schedule data violates a schedule norm20614. In response to the determination that a schedule property 20134of the output schedule data 20604 violates a schedule norm 20614, theschedule warden circuit 20612 generates a corrective action commandvalue 20618 and transmits the corrective action command value 20618 toone or more connector circuits 20608. In embodiments, the correctiveaction command value 20618 may be structured to trigger an adjustment toa connector circuit 20608 resulting in a change to a schedule input20610 or the schedule data 20604 such that the resulting output scheduledata 20604 no longer violates a schedule norm 20614. In embodiments, thecorrective action command value 20618 may correspond to an alertmessage, or a change in a bias of a connector circuit 20608, such asincreasing or decreasing a weighting 20620 of an output of a module,such as an external model 410, a primary business model 424, a secondarybusiness model 414, as shown in FIG. 4 , and/or any othermodel/module/circuit as described elsewhere herein. The correctiveaction command value 20618 may correspond to a direct scheduling changeresulting in a change to schedule properties 20134 such that thecorresponding schedule properties 20134 no longer violate, or violatesless, a schedule norm 20614 threshold.

Referring to FIG. 43 , an apparatus 20700 for adjusting schedules isdepicted. The apparatus 20700 includes a schedule interpretation circuit20702 structured to interpret schedule data 20704 and identify scheduleproperties 20708, and a warden circuit 20710 structured to generate aplurality of scores 20712 based on the identified schedule properties20708. The warden circuit 20710 is further structured to retrieve aplurality of baseline values 20722 corresponding to one or more of theschedule properties 20708 and/or combinations of schedule properties20708. The warden circuit 20710 is further structured to determinewhether the schedule data 20704, a particular schedule property 20708,and/or a combination of schedule properties are out of alignment withone or more of the baseline values 20722 to an extent that a correctiveaction should be taken. The apparatus 20700 further includes acorrective action circuit 20714 structured to generate a correctiveaction command value 20718. The corrective action command value 20718 isgenerated based, at least in part, in response to a determination by thewarden circuit 20710 that a corrective action is required, where thecorrective action command value 20718 is structured to effect a changeto at least the schedule property 20708 that is out of alignment. Thechange may be directly to the schedule data and/or to a connector, asdisclosed herein. A corrective action provisioning circuit 20720 maythen transmit the corrective action command value 20718.

The systems and methods described herein for a schedule warden providevarious technical benefits to processing of schedules using a computer.In one aspect, the systems and methods provide for efficient comparisonand evaluation of schedules using automated methods. The system andmethods provide for efficient identification and representation ofschedule features that may be compared and evaluated using a computer.Comparison of schedules using automated methods has traditionally beenrestricted to a small number of limited/simple features, with other,more complex, quantitative features having been difficult to capture andidentify. As will be appreciated, in one aspect, the methods disclosedherein provide for efficient capture of quantitative features of aschedule such that they may be efficiently processed using a computer.In one example, trained models and historical data is used to identifyqualitative features for comparison and scoring. Qualitative featuresmay be compared and manipulated using quantitative scores therebyallowing efficient and predictable analysis of schedules.

In embodiments, the Continuous Model Validator may detect anomalies (aprediction or group of predictions that are inaccurate (andstatistically outside of expected random variances). The ContinuousModel Validator may work across companies, industries, and regionslooking for patterns that might indicate what type of data the systemmight be missing. For example, embodiments may identify when one or morevariables are missing, when/if extra runs are needed (or would bebeneficial), etc. Embodiments may also try to remove variables, e.g.,simplify the model, etc. As will be appreciated, embodiments providingfor autonomous or semi-autonomous addition/subtraction of newvariables/models, may utilize some form of an AB side-by-side testingenvironment. Solutions using the new A agglomerate models and the oldcollection of B agglomerate models may be run side-by-side to see if thenew models improve accuracy and/or the performance of the system. Inaddition to improving prediction, some embodiments may accept less (butstill acceptable) accuracy where using a given set of models requiressignificantly less time to process.

Embodiments may include aspects for new feature and/or variablediscovery. In embodiments, when the Continuous Model Validator detectsan anomaly, e.g., when a prediction or group of predictions areinaccurate upon post-inspection, the new feature/variable discoverymodule may kick-in and try to determine what variable(s) might be inputto the agglomerate models (with appropriate model modifications and/ornew model connectors. For instance, if multiple retail businesses in aregion had much greater traffic and scheduling inaccuracies over a givenweekend, but the agglomerate models properly predicted items forbusinesses that are not sensitive to retail buying patterns, the systemmay search for a previously unknown event in the region. The system mayscan the news and/or other sources (including other correctly modeledbusinesses that may have taken into account an event not modeled byother businesses in the region). If identified, the system may testwhether the addition of a variable and a new bias connector could haveeffectively captured the event by performing ex post facto A/B.

In another case, detected issues (e.g., those detected via a continuousmodel validator, as disclosed herein) may exist across all businesses ina region (search for local disaster, weather event, concert event,conference event, sporting event, and/or a local event of such magnitudethat it effects all businesses in a region (open of hunting season as anexample)).

In yet another case, the detected issue may affect certain jobcategories, or all businesses within a state/country. In this case, thesystem may look to macro-economic conditions and/or personnel shortageswithin a given job category. For instance, during a pandemic, allbusinesses might have been affected, potentially requiring some updatesto the agglomerated models. Accordingly, the system may determine thatsome business types/job categories have been affected more than others.

Embodiments of the system, as described herein, may look forcorrelated/predictive features that can be extracted from a given source(number of news articles, gov′t sites, etc.) and can be used to buildnew model connectors that can be trained to accommodate and/or adjustthe results based on the new variable. For example, if shown to beeffective, the system may build new machine learning (ML) models (orsuggest the building of a new ML model), that incorporates the newvariable.

In embodiments, methods and systems for proposing and executingscheduling experiments may be provided, and optionally be included incomponents/modules 146 and/or 148 (FIG. 1 ). The experiments may besimulated and/or conducted in the real world with AI learning from theresults. The selection of executed experiments, implementation ofchanges based on the results, and the like, may be automatic and/ormanual. Embodiments may provide for dials and/or sliders that providefor the introduction of how much risk (e.g., poor outcome) a user of thesystem can tolerate. Embodiments may provide for employees to opt-in toan experiment for an incentive, e.g., $1.00 more/hour, such as where theexperiment provides a more dynamic schedule, or provide for an employeeto opt-out of the experiment, such as to keep a more predictableschedule. Embodiments of schedule experimentation may be a module thatreceives inputs, e.g., a schedule and/or other data, e.g., biases, as:direct input, i.e., the schedule experimentation module may act as astandalone module; as direct input to an agglomerate network, e.g.,without use of connectors; and/or from connectors, e.g., the scheduleexperimentation module is one of a plurality of modules within anagglomerate network. Schedule experimentation may take the form of aschedule generation module within an agglomerate network that passes itsoutput (e.g., schedules) to other modules in the agglomerate network forevaluation where the other modules generate output(s), e.g., a bias. Theother modules may, in turn, feed the output back into the scheduleexperimentation module to form a feedback loop which tries to reachequilibrium and/or optimization of various biases in the agglomeratenetwork while keeping the generated schedules comparable to onesgenerated by managers. The connections between the scheduleexperimentation module and the various other modules of the agglomeratenetwork may be accomplished via connectors.

Referring to FIG. 44 , a method 150100 may be provided. The method150100 includes receiving 150102 schedule data 150112 corresponding to aschedule 150114; receiving 150104 a schedule modification parameter150116; determining 150106, based at least in part on the schedule data,a schedule feature 150118 of the schedule; identifying 150108 a set ofincentives 150120 for employees for the schedule feature; and generating150110 a plurality of experimental schedules 150122 based on theschedule modification parameter, wherein the plurality of experimentalschedules is configured to test the effectiveness 150124 of differentincentives of the set of incentives on the schedule features. Referringto FIG. 45 , certain further aspects of the method 150100 are describedfollowing, any one or more of which may be present in certainembodiments. For example, schedule modification parameters may includerisk tolerance 150202. The schedule features may be features withhistorically low employee coverage 150204. The schedule features may beundesirable features of the schedule for employees. The incentive may bemonetary, paid time off, and the like. The employees may be incentivizedto participate in the experiments. The employees may opt in 150206and/or opt out 150208 for the experiments. The difficult schedulefeatures 150210 may include at least one of consecutive time slots, lateshifts, busy shift times, and the like.

Referring to FIG. 46 , an apparatus 150300 may be provided. Theapparatus 150300 includes a historic schedule interpretation circuit150302 structured to: interpret 150308 historical schedule data 150318;and extract 150310 a difficult schedule feature 150320 from thehistorical schedule data; an incentive determination circuit 150304structured to identify 150312 a set of incentives 150322 compatible withthe difficult schedule feature; a schedule experimentation circuit150306 structured to: receive 150314 schedule modification parameters150326; and generate 150316, based at least in part on the schedulemodification parameters, a set of experimental schedules 150324 eachwith different incentives of the set of incentives.

Referring to FIG. 47 , certain further aspects of the apparatus 150300are described following, any one or more of which may be present incertain embodiments. The schedule modification parameters may includerisk tolerance 150402. The difficult schedule feature may be a featurewith historically low employee coverage. The difficult schedule featuremay be an undesirable feature of a schedule for an employee. The set ofincentives may be monetary, paid time off, and the like. The difficultschedule feature may be consecutive time slots, late shifts, busy shifttimes, and the like.

Referring to FIG. 48 , an agglomerate network 150500 may be provided.The agglomerate network 150500 includes an agglomerate network forgenerating experimental schedule data, the agglomerate network includinga scheduler circuit 150502 structured to output schedule data 150522; aconnector circuit 150504 structured to adjust at least one of an inputto the scheduler circuit or the schedule data outputted by the schedulercircuit based on a set of experimental biases 150524; and a scheduleexperimentation circuit 150506 structured to: receive 150510 schedulemodification parameters 150526; and generate 150512 the set ofexperimental biases for the connector circuit, wherein the set ofexperimental biases are generated based at least in part on the schedulemodification parameters; transmit 150514 the set of experimental biasesto the connector circuit; and a schedule evaluation circuit 150508structured to: evaluate 150516 the schedule data for performance 150528;and determine 150518 when the performance is below a threshold 150530and, in response, modify 150520 the schedule modification parameters.

Referring to FIG. 49 , certain further aspects of the agglomeratenetwork 150500 are described following, any one or more of which may bepresent in certain embodiments. The schedule modification parameters mayinclude risk tolerance 150602.

Embodiments may include a semi-autonomous experiment controller. Inembodiments, the semi-autonomous experiment controller may operate inconjunction with the semi-autonomous goal setter to better understandthe impact of a given variable on an intermediate or final goal(s). Forexample, a given goal (such as, for example, low employee attrition) maybe selected as a possible target of an experiment. The target goal maybe unacceptably low/high compared with other employers or compared withother goals or ideal goals. The experiment controller, in some cases,may modify one or more inputs/constraints to an agglomerate network ormodel, generate one or more new or updated schedules, and monitor how itaffects the target goal after one or more iterations of scheduling. Ifthe target goal is affected within particular confidence, the experimentcontroller may speculate that the modified inputs had a certaincausative effect on the target goal.

In some cases, to increase confidence, the experimental controller mayreplicate the speculated causative effect on other employers, otherschedules, other agglomerate networks, agglomerate models, other goalsand the like.

Additionally, the experiment controller may search for comparableemployers to run experiments with. Such employers may be similar incertain metrics or attributes such as size, number of employees,revenue, profit, geography, segment, industry, owners, etc. Suchemployers may have low/high or similar/opposite scores on a target goalor a potentially related or correlated goal. Running the same experimenton this similar employer may help verify whether the target goals arereally affected by the modified inputs/constraints.

In some cases, the systems and methods may present proposed experimentsto an administrator to allow them to confirm the running of anexperiment or prioritize various experiments. In other cases, thesystems and methods may be allowed to decide to experiment on their ownfor certain goals or when expected results on a goal are under or over athreshold. As the systems and methods make consistently good decisions,the systems and methods may be allowed to do more experiments on theirown by updating the threshold. In various cases, the systems and methodsmay present the output results of the experiments on a report or GUI toan administrator. The system may output the experiment, the results, andrecommendations from the results. The systems and methods may be allowedto implement certain recommendations based on its confidence and basedon its track record of successful decisions. An administrator mayoversee, override, or confirm different decisions from the experimentcontroller.

A non-limiting example of a method of the semi-autonomous experimentcontroller is shown in FIG. 50 . The method 1100 may include generatingexperiments to determine cause and effect between one or more inputsand/or constraints. The method may include modifying at least one inputand/or constraint to an agglomerate network model 1102. The agglomeratenetwork model may generate an updated schedule using the modified inputsand/or constraints 1104. The method 1100 may further include monitoringthe schedule to evaluate the effects of the modifications of the inputsand/or constraints 1106 on one or more goals. The effects on the goalsmay be correlated to modifications of the inputs and/or constraints.Modifications that resulted in positive effects on goals may bereplicated for other schedules by modifying inputs and/or constraints1108.

Embodiments may include detecting unknown variables. Embodiments of thecurrent disclosure may provide for the identification of variables thataffect a schedule, but which may not have been previously identified asbeing a contributing factor. For example, while inputting data from afirst source, the system may identify summary, ancillary, secondary, orother variables. The system may track these variables, and as the valueschange over time, it may be determined whether those values correlatewith the quality or performance of the agglomerate networks, models, orschedules.

In a simple example, the system may be extracting information from aweather system. The actual forecast may be the primary weather input tothe system, but the system may notice a new value called “Days ofDrought” or “flood level” of a river or other such values. The new valuemay be numerically displayed, or it may be embedded in a news article onthe website that is parsed and understood by an NLP engine. The systemmay track these new variables and see how they correlate with scheduleneeds, requirements, or performance. In one simple example, as the “Daysof Drought” value increases, a business, such as one in the agriculturalindustry, has fewer scheduling needs or requirements. Conversely, afast-food restaurant may notice that its scheduling needs orrequirements are unchanged as the “Days of Drought” increases. In such ascenario, the agriculture industry may learn that it is correlated withthe new variable, but the fast-food industry may learn that it is notcorrelated with it.

Embodiments may include an agglomerated input handler. Embodiments ofthe current disclosure may include an agglomerated input handler thatcollects inputs, and/or generates events. The input handler may parse,extract, format, or otherwise manipulate the inputs into forms that canbe understood or acted upon by other agglomerate networks, models,schedules, and the like. In some cases, the input may convert schedules,or portions thereof, to embeddings as is described in more detailherein. In other cases, one or more values may be used as features thatare input into various agglomerate networks, models, or schedules. Forexample, snowfall in inches may be one feature, and the percentagechance of precipitation may be another. In some cases, a NaturalLanguage Processor (NLP) engine must first parse previously unknown datato find variables or inputs of interest such as finding the “Days ofDrought” metric from parsing data on a weather webpage as discussedherein. Once inputs are identified by the input handler, the system maysend events to various circuits, agglomerated networks, models, orschedules.

Embodiments may include an agglomerated metrics analyzer. Embodiments ofthe current disclosure may include an agglomerated metrics analyzer thatproduces quality/confidence metrics that may span the agglomeratedmodels. The metrics analyzer may help determine which agglomeratednetworks or models or schedules are contributing to improvements, andwhich are not. Those agglomerated networks or models or schedules whichare improving may be given more weight in final decisions, more weightin allowance to use system resources, more iterations of refinements tofinal outputs, etc. In some cases, the systems and methods may choose tooutput some or all of these metrics to GUIs, reports, or logs so thatsystem administrators can monitor the behavior and performance of thesystems and methods. In some cases, the systems and methods mayhighlight ambiguous, concerning, or exceptional metrics to help theadministrator understand the systems and methods.

Embodiments may include an agglomerated output composer. Embodiments ofthe current disclosure may include an agglomerated output composer thatproduces selected agglomerate models for output to users. In some cases,the systems and methods may choose to output some or all of these modelsto GUIs, reports, or logs so that system administrators can monitor thebehavior and performance of the systems and methods. In some cases, themodels may be output in raw form, in summarized form, in detailed form,in numerical form, in textual form, in graphical form, in a scheduleform, in a calendar form, or in any other form. In some cases, thesystems and methods may highlight ambiguous, concerning, or exceptionalmodels to help the administrator understand the systems and methods.

Embodiments may include an interactive user interface, e.g., component120 (FIG. 1 ). Embodiments of the current disclosure may include aninteractive user interface for interacting with the system and/orindividual agglomerated networks, as described herein. For example, inembodiments, one or more user interfaces may provide for receiving userfeedback for schedules, portions of schedules, and/or features ofschedules. In embodiments, the one or more user interfaces may providefor communicating to an operator data features that used by theagglomerated networks to edit or remove elements of the data. Inembodiments, the one or more user interfaces may provide for employeefeedback on the “employee profile” they are associated with. Suchfeedback may include making suggestion and/or editing of the profilethey are associated with. Such feedback may also include associatingthemselves with a different profile. Such feedback may also includeliking and/or disliking aggregations or implications of a schedule,e.g., total hours/overtime, hours working with other particularemployees, etc.

In embodiments, the one or more user interfaces may also provide foremployer feedback and/or similar admin functions for use on behalf ofthe employer. For example, a company can “like” or “dislike a scheduleand/or one or more portions of a schedule. Non-limiting examples ofschedule portions include portions of a schedule as well as aggregationsor implications of the schedule, e.g., total cost, total hours,educational metric, percent of reusable utilities, end customersatisfaction, other HR metrics, degrees of connectedness across socialaspects (where a high score for a schedule may mean people generallywill be friendly and enjoy who they are working with, and where low mayimply they just work together).

Embodiments of the one or more user interfaces may also provide for auser to add and/or edit one or more axes of a chart/graph generated byembodiments of the system, as described herein.

Embodiments may include agglomerated model driven machine learningmodels. Embodiments of the current disclosure may include predictive andgenerative machine learning models, driven by other Agglomerated Models.For example, utilization schedules may be generated by a recurrentneural network (RNN) or an attention-based transformer network such as agenerative pre-trained transformer (GPT), trained on a corpus ofhistorical schedules and additional features such as weather and laborforecast. These generative models may be driven by other AgglomerateModels, such as decision-tree-based forecasting models providing weatherpredictions, employee sentiment predictions, and labor predictions.

Embodiments may include external data sources. External data sources maybe provided. The external data sources may be from websites, databases,agglomerated networks, models, constraints, or computers outside of theagglomerated network system including external data sources owned by thesame company or entity that owns or runs the agglomerated networks orowned by an external company or others. The external data sources mayinclude machines such as scanners and fax machines which take paperdocumentation and convert it into digital information which can be usedby the systems and methods.

Embodiments may include external outputs. The external outputs may be towebsites, databases, agglomerated networks, models, schedules, orcomputers outside of the agglomerated network system including externaloutputs owned by the same company or entity that owns or runs theagglomerated networks or owned by an external company or others. Theexternal outputs may be to printers and printouts. In some cases, thesystem may output some or all of the outputs to external GUIs, externalreports, or external logs. In some cases, the outputs may be in rawform, in summarized form, in detailed form, in numerical form, intextual form, in graphical form, in a schedule form, in a calendar form,or in any other form.

Embodiments may include methods (including AI methods) for traininggenerative resource utilization models. Models including theagglomerative network may be driven by optimization engines to identifythe best resource utilization. They may also be generative modelstrained through machine learning to mimic historically implementedscheduling styles; or they may use combinations of optimization,historical training, and other approaches, such as, but not limited to,backtracking, TABU search, or simulated annealing. Machine learningmodels may be trained through the encoding of historical schedules, suchas run-length encoding and GANTT encoding described herein, in order toproduce a generative model such as a generative adversarial network oran attention-based transformer network.

Embodiments may include schedule representation of job assignments.Models produced through training on historical schedules must accountfor variable differences in the training data set due to drift over timeor due to variability in the data. For example, a model must be able totrain on historical jobs that may have been phased out, and on employeeswho are no longer available to work as resources in the model's targetschedule. Furthermore, the model may train on historical schedules fromother departmental units in the target organization or from completelydifferent organizations. One non-limiting example of the need for suchtraining is to increase the size of the historical data set; another isthe cold start problem encountered during roll-out of new organizationsor organizational units, where the target organization did notpreviously exist. In these cases, models may train and generateassignments using profiles of the variables rather than the specificvariables. A mapping model then converts the profile to the targetresource which matches most closely to the profile. One example of sucha profile is neural network embedding, discussed herein.

Embodiments of one or more components/modules disclosed herein, e.g.,124, 126, 128 (FIG. 1 ), may include and/or use embeddings fortimekeeping and/or scheduling. For example, employee embeddings mayrepresent an employee profile, permitting AI training to consume theemployee profile as feature input rather than a specific employee. As anon-limiting example use case, an employee embedding may represent anyemployee suitable for working in an administrative capacity; in anothernon-limiting use case, an employee embedding may represent part-timeemployees whose work patterns demonstrate a need for flexibility relatedto local school calendars. In another non-limiting example use case, thesystem may classify known employees (within a business, industry, orchain of businesses, e.g., a franchise), and recommend one or moreprofiles and/or templates thereof. Yet another non-limiting use case mayinvolve a hybrid approach where an admin function defines someembeddings and the system finds other embeddings, or vice versa.

Other non-limiting examples of embeddings used by the agglomeratenetworks, and the systems and methods described herein, include job,position, department, store, industry vertical, and geographicalembeddings.

The creation of embeddings may occur through distillation of the datafeatures consumed by components of the agglomerate networks, and thesystems and methods described herein. In one example, a variationalautoencoder (VAE) may produce neural weights in its inner layers fromtraining on assorted time windows of timecard punch, timekeeping ruleexceptions, and punch versus schedule data, combined with informationabout the related industry vertical. In this non-limiting example, theseneural weights represent vector embeddings of timekeeping behavior whichcan be propagated through an agglomerate network connector which usesthe embeddings as data features. These data features, in combinationwith the industry vertical as an additional input feature, convey thework behavior of that industry vertical in all modes of networkoperation, including training, prediction, and generation.

As disclosed in greater detail, embodiments of the current disclosureprovide for timekeeping and scheduling. In embodiments, application andadaptation to use employee embeddings for timekeeping and scheduling aredisclosed. In embodiments, employee embeddings are used as a trainingset to train an AI-based model on characteristics of employee profilesinstead of a specific employee and generate a schedule. The training setcorresponds to employee embeddings determined from all employee data orfrom employee data selected from a pool of employee data. An employeeembedding represents a plurality of employee data by capturing commonemployee characteristics. In embodiments, an administrator may selectthe employee data used for which employee embeddings are determined. Inother words, an administrator may define the types of employee profilesfor which employee embeddings are to be determined. In embodiments, thetimekeeping and scheduling apparatus may classify, e.g., group based onone or more common properties/attributes, known employees (at thatbusiness, industry, or chain of businesses) and recommend employees(e.g., recommend employee profiles). In other words, employees may beclassified based on employee embeddings, and employee profilesrepresented by the employee embeddings may be recommended. The apparatusin some embodiments may provide for a hybrid scheme where theadministrator defines some employee embeddings (e.g., an administratordefines a subset of the employee data to generate embeddings for), andthe apparatus defines others or vice versa.

In embodiments, an employee embedding may include, e.g., capture,characteristics of elements such as a time block, employee profile,schedule constraints, and the like. The employee embeddings capture somerelations between the elements, e.g., characteristics). Employeeembeddings can be numbers or vectors that encode the elements such thatthe elements that are close in value/vector space have similarcharacteristics and may be interchangeable and/or compatible. Inembodiments, the employee embeddings may be used as inputs to neuralnetworks that enable the processing of schedules, e.g., schedule data,and constraints using neural networks or other machine learningalgorithms.

The disclosed apparatus may include scheduling circuits, e.g., moduleswith models, e.g., AI models and machine learning models, trained withemployee embeddings that may be disposed of within an agglomeratenetwork where other modules evaluate schedules generated by thescheduling circuit. The other modules may, in turn, feed their outputsinto the scheduling modules to form a feedback loop that tries to reachequilibrium and/or optimize various biases in the agglomerate network.

Accordingly, referring to FIG. 51 , an apparatus 90100 for timekeepingand scheduling is shown in accordance with an embodiment of the currentdisclosure. The apparatus 90100 may be embodied via one or moreprocessors on one or more electronic devices, e.g., servers,workstations, smart devices, etc. In embodiments, the apparatus 90100may form part of an agglomerate network. In embodiments, the apparatus90100 may be apart from an agglomerate network, e.g., a standalonedevice that can interact with other devices. As shown in FIG. 51 , theapparatus 90100 includes an employee surveyor circuit 90102, anembedding generator circuit 90104, artificial intelligence circuit90106, scheduling circuit 90108, and schedule provisioning circuit90110. Each of the aforementioned circuits is described herein. Theemployee surveyor circuit 90102 is structured to interpret employee data90112. The employee surveyor circuit 90102 may receive employee data90112 from storage and collate the employee data 90112 for furtherprocessing in the embedding generator circuit 90104. The embeddinggenerator circuit 90104 is structured to determine employee embeddings90114 based at least in part on employee data 90112. The generatedemployee embeddings 90114 are processed in the artificial intelligencecircuit 90106, which is structured to generate a model 90116 based atleast in part on the employee embeddings 90114. The model 90116 isfurther processed in the scheduling circuit 90108 to generate theschedule data 90118, which represents a schedule with employees assignedto shifts and information related to the shifts. The schedule data 90118is transmitted by the schedule provisioning circuit 90110 to anagglomerate network or other modules for outputting on a screen orstoring.

Illustrated in FIG. 52 is another apparatus 90200 for timekeeping andscheduling, in accordance with another embodiment of the currentdisclosure. The apparatus 90200 may form part of an agglomerate networkor be apart from an agglomerate network. The apparatus 90200 may includean employee surveyor circuit 90102, an embedding generator circuit90104, artificial intelligence circuit 90106, scheduling circuit 90108,and schedule provisioning circuit 90110, as discussed above. Inembodiments, the employee data 90112 has at least one of an employeeprofile 90201, a schedule constraint 90202, or a time block 90203. Theemployee profile 90201 may include an employee's name, address,experience, duties, and the like. The schedule constraints 90202 mayhave information related to the employee profiles, such as employeeavailability and conflict with other employees. Time block 90203 mayinclude employee duties at different blocks of time (e.g., dividing ashift into blocks for lunchtime, replenishing merchandise, working as acashier, etc.).

In embodiments, employee embeddings 90114 are generated from employeedata 90112 by the embedding generator circuit 90104. In embodiments, theemployee embeddings 90114 determined from employee data having similarcharacteristics are closer in space than the employee embeddings 90114determined from employee data having discrete characteristics.Therefore, instead of using employee data 90112 directly for scheduling,employee embeddings 90114 are used. In embodiments, the employeeembeddings 90114 capture several characteristics of the employee data90112. In other embodiments, employee embeddings 90114 operate as aclustering method to cluster several characteristics of employee data90112 into an employee embedding. In other embodiments, employeeembeddings 90114 may include projecting employee data 90112 into adifferent space. In embodiments, high dimensional data, e.g., aplurality of characteristics in the employee data 90112 may berepresented by a lower number of characteristics in the employeeembeddings 90114. Therefore, employee embeddings 90114 capture some ofthe characteristics in the employee data 90112 and represent employeedata 90112 of similar characteristics closer to each other in the space.Further, employee embeddings 90114 may focus less on characteristicsdetermined to be less important. In example embodiments, differentspaces may be used where employee data 90112 of similar characteristicsmay be closer in, to name a few: an affine space, a projective space, acurved space, a Euclidean space, and a pseudo-Euclidean space. Employeedata 90112 close to each other in one space may not necessarily be closein another.

In embodiments, not all employee data 90112 are used by the embeddinggenerator circuit 90104 to generate employee embeddings 90114. Forinstance, only a subset of the employee data 90112 selected from alarger pool of employee data may be used. In embodiments, at least partof the employee data used to determine the employee embeddings areselected from the larger pool of employee data by an administrator. Inembodiments, the employee surveyor circuit 90102 is further structuredto select at least part of the employee data from the larger pool ofemployee data.

In embodiments, the artificial intelligence circuit 90106 is based onmachine learning (e.g., machine learning circuit 90204), responsible forperforming machine learning operations such as training and inference.Therefore, in some examples, the model 90116 is a machine learning model90206. In embodiments, the machine learning model 90206 is generatedusing at least one of: supervised training, semi-supervised training,and unsupervised training 90208. In embodiments, the model 90116 can bea neural network model 90210. In other examples, the model 90116 can bea deep learning model 90212.

The model 90116 is trained using employee embeddings 90114. This model90116 is used to generate the schedule data 90118 by the schedulingcircuit 90108. The schedule data 90118 may include employee embeddings90114 with respective shift information. The schedule data 90118 mayinclude information related to employee data 90112 of a plurality ofemployees represented by the employee embeddings 90114. In otherexamples, the schedule data 90118 may include the employee data 90112having employee profiles 90201 with assigned shifts.

In embodiments, the apparatus 90200 may be structured to generatetimekeeping records 90214, which includes clock in and clock out timesfor employees, (g. employee profile. Timekeeping records 90214 may alsoinclude the length of employment, the number of shifts worked, and thedates of such shifts. In embodiments, the scheduling circuit 90108 maybe further structured to generate a list of recommended employees 90216.The list of recommended employees 90216 may be a list of recommendedemployees for an administrator to consider when assigning shifts. Thelist of recommended employees 90216 may consider characteristics such asthe experience of employees, workload on certain days, synergy betweenemployees, etc. In embodiments, the recommended list of recommendedemployees 90216 may not be part of the schedule data 90118. Inembodiments, the scheduling circuit 90108 may utilize input from otherartificial intelligence modules (e.g., circuits) to generate theschedule data 90118.

Shown in FIG. 53 is a flowchart of a method 90300 for timekeeping andscheduling, in accordance with an embodiment of the current disclosure.The method 90300 may be performed by the apparatus 90100 (FIG. 51 ), theapparatus 90200 (FIG. 52 ), and/or any other computing device disclosedherein. The method 90300 may include interpreting, via an employeesurveyor circuit 90102 (FIG. 51 ), employee data 90302 and determining,via an embedding generator circuit 90104 (FIG. 51 ), employee embeddingsbased at least in part on the employee data 90304. Further, the method90300 includes generating, via an artificial intelligence circuit 90106(FIG. 51 ), a model based at least in part on the employee embeddings90306 and generating, via a scheduling circuit 90108 (FIG. 51 ),schedule data via the model 90308. After generating the schedule data,the method 90300 may also include transmitting, via a scheduleprovisioning circuit 90110 (FIG. 51 ), the schedule data 90310.

Illustrated in FIG. 54 is a flowchart of a method 90400 for timekeepingand scheduling in accordance with embodiments of the current disclosure.The method 90400 performs the operations of method 90300 (FIG. 53 ). Themethod 90400 may be performed by the apparatus 90100 (FIG. 51 ) and/orany other computing device disclosed herein. In embodiments, theemployee data 90402 of method 90400, interpreted by the employeesurveyor circuit 90102 (FIG. 51 ), has at least one of: an employeeprofile 90404, a schedule constraint 90401, or a time block 90403. Theemployee profile 90404 may include an employee's name, address,experience, and duties. The schedule constraints 90401 may haveinformation related to the employee profiles, such as employeeavailability and conflict with other employees. Time block 90403 mayinclude employee duties at different blocks of time (e.g., dividing ashift into blocks for lunchtime, replenishing merchandise, working as acashier, etc.).

In embodiments, employee embeddings 90406 of method 90400 are generatedfrom employee data 90402 by the embedding generator circuit 90104 (FIG.51 ). In example embodiments, the employee embeddings 90406 determinedfrom employee data having similar characteristics are closer in spacethan the employee embeddings determined from employee data havingdiscrete characteristics. Therefore, instead of using employee data90112 directly for scheduling, employee embeddings 90406 are used. Inembodiments, the employee embeddings 90406 capture severalcharacteristics of the employee data 90402. In other embodiments,employee embeddings 90406 operate as a clustering method to clusterseveral characteristics of employee data 90402 into an employeeembedding. In other embodiments, employee embeddings 90406 may includeprojecting employee data 90402 into a different space. In someembodiments, the high dimensional data, e.g., a plurality ofcharacteristics in the employee data 90402 may be represented by a lowernumber of characteristics in the employee embeddings 90406. Therefore,employee embeddings 90406 capture some of the characteristics in theemployee data 90402 and represent employee data 90112 of similarcharacteristics closer to each other in the space. Further, employeeembeddings 90406 may focus less on characteristics determined to be lessimportant. In example embodiments, different spaces may be used whereemployee data 90402 of similar characteristics may be closer in, to namea few: an affine space, a projective space, a curved space, a Euclideanspace, and a pseudo-Euclidean space. Employee data 90402 close to eachother in one space may not necessarily be close in another.

In embodiments, the method 90400 may include selecting a subset of theemployee data 90402 by the employee surveyor circuit 90102 (FIG. 51 ),the subset being selected from a larger pool of employee data. Employeeembeddings 90406 may be determined for the subset by the embeddinggenerator circuit 90104 (FIG. 51 ). In embodiments, at least part of theemployee data 90402 used to determine the employee embeddings areselected from the larger pool of employee data by an administrator. Inembodiments, the method 90400 may include selecting at least part of theemployee data 90402 from the larger pool of employee data.

In embodiments, the model 90408, generated by the artificialintelligence circuit 90106 (FIG. 51 ) can be based on machine learning,the generated model being a machine learning model 90410. Inembodiments, the machine learning model 90410, generated by the machinelearning circuit 90204 (FIG. 52 ), is generated using at least one ofsupervised training, semi-supervised training, and unsupervised training90412. Further, the model 90408 may be a neural network model 90414. Inother examples, the model 90408 may be a deep learning model 90416. Themodel 90408 can be trained using employee embeddings 90406. This model90408 can be used to generate the schedule data 90418 by the schedulingcircuit 90108 (FIG. 51 ). The schedule data 90418 may include employeeembeddings 90406 with respective shift information. The schedule data90418 may include information related to employee data 90402 of aplurality of employees represented by the employee embeddings 90406. Inother examples, the schedule data 90418 may include the employee data90402 having employee profiles 90404 with assigned shifts.

In embodiments, the method 90400 may include generating timekeepingrecords 90420 by the scheduling circuit 90108 (FIG. 51 ). Timekeepingrecords 90420 include clock in and clock out times for employees (e.g.,employee profile). Timekeeping records 90420 may also include the lengthof employment, the number of shifts worked, and the dates of suchshifts. In embodiments, the method 90400 may further include generatinga list of recommended employees 90422 by the scheduling circuit 90108(FIG. 51 ). The list of recommended employees 90422 may be a list ofrecommended employees for an administrator to consider when assigningshifts. The list of recommended employees 90422 may considercharacteristics such as the experience of employees, workload on certaindays, synergy between employees, etc. In example embodiments, the listof recommended employees 90422 may not be part of the schedule data90418. In embodiments, the method 90400 may utilize input from otherartificial intelligence modules, e.g., circuits, to generate theschedule data 90418.

Illustrated in FIG. 55 is another method 90500 for timekeeping andscheduling, in accordance with an embodiment of the current disclosure.The method 90500 may be performed by the apparatus 90100 (FIG. 51 ), theapparatus 90200 (FIG. 52 ), and/or any other computing device disclosedherein. The method 90500 includes determining, using an embeddinggenerator circuit 90104 (FIG. 51 ), employee embeddings 90502. Further,the method 90500 includes generating, via an artificial intelligencecircuit 90106 (FIG. 51 ), a model using the employee embeddings 90504,and generating, via a scheduling circuit 90108, a timekeeping recordusing the model and the employee embeddings 90506.

FIG. 56 is another method for timekeeping and scheduling, in accordancewith an embodiment of the current disclosure. The method 90600 performsthe operations of method 90500 (FIG. 55 ). The method 90600 may beperformed by the apparatus 90100 (FIG. 51 ) and/or any other computingdevice disclosed herein. In embodiments, the model 90602, generated bythe artificial intelligence circuit 90106 (FIG. 51 ), is based onmachine learning. Hence, the model 90602 can be a machine learning model90604. In embodiments, the machine learning model 90604, generated bythe machine learning circuit 90204 (FIG. 52 ), is generated using atleast one of: supervised training, semi-supervised training, andunsupervised training 90606. Further, the model 90602 may be a neuralnetwork model 90608. In other examples, the model 90602 may be a deeplearning model 90610.

In embodiments, the timekeeping records include clock in and clock outtimes for employees, e.g., employee profile. Timekeeping records mayalso include the length of employment, the number of shifts worked, andthe dates of such shifts.

Illustrated in FIG. 57 is another method 90700 for timekeeping andscheduling, in accordance with an embodiment of the current disclosure.The method 90700 may be performed by the apparatus 90100 (FIG. 51 ), theapparatus 90200 (FIG. 52 ), and/or any other computing device disclosedherein. The method 90700 includes determining, using an embeddinggenerator circuit 90104 (FIG. 51 ), employee embeddings 90702. Further,the method 90700 includes generating, via an artificial intelligencecircuit 90106 (FIG. 51 ), a model using the employee embeddings 90704,and generating, via a scheduling circuit 90108, a list of recommendedemployees using the model and the employee embeddings 90706.

FIG. 58 is another method for timekeeping and scheduling, in accordancewith an embodiment of the current disclosure. The method 90800 performsthe operations of method 90700 (FIG. 57 ). The method 90800 may beperformed by the apparatus 90100 (FIG. 51 ) and/or any other computingdevice disclosed herein. In embodiments, the model 90802, generated bythe artificial intelligence circuit 90106 (FIG. 51 ), is based onmachine learning. Hence, the model 90802 can be a machine learning model90804. In embodiments, the machine learning model 90804, generated bythe machine learning circuit 90204 (FIG. 52 ), is generated using atleast one of: supervised training, semi-supervised training, andunsupervised training 90806. Further, the model 90802 may be a neuralnetwork model 90808. In other examples, the model 90802 may be a deeplearning model 90810. In embodiments, the list of recommended employeesincludes employee embeddings. In other example embodiments, the list ofrecommended employees includes employee profiles.

An example embodiment of the present disclosure, utilizing one or moreaspects as set forth preceding, includes a method for predicting and/ordetermining schedules. The method includes generating a first schedulevia a first agglomerate network, and passing one or more portions of thefirst schedule to a second agglomerate network as input via a connector.The method further includes generating a second schedule via the secondagglomerate network based at least in part on the one or more portionsof the first schedule, and transmitting the second schedule. In certainembodiments, the method further includes weighting at least one of thefirst or the second agglomerate network to favor an employer over anemployee. In certain embodiments, the method further includes weightingat least one of the first or the second agglomerate network to favor anemployee over an employer. In certain embodiments, the method furtherincludes mixing at least one of the first schedule or the secondschedule with at least one other schedule.

Another example embodiment of the present disclosure, utilizing one ormore aspects as set forth preceding, includes a system for predictingschedules. The system includes a plurality of agglomerate networks, oneor more connectors, a schedule selector circuit, and a scheduleprovisioning circuit. The plurality of agglomerate networks are eachstructured to generate a corresponding schedule. The one or moreconnectors are each structured to pass at least one of the schedules asinput to at least one of the plurality of agglomerate networks. Theschedule selector circuit is structured to select at least one of theschedules. The schedule provisioning circuit is structured to transmitthe selected schedule.

Another example embodiment of the present disclosure, utilizing one ormore aspects as set forth preceding, includes an apparatus that includesa scheduling factor interpretation circuit, one or more agglomeratenetwork circuits, one or more connector circuits, a schedule selectorcircuit, and a schedule provisioning circuit. The scheduling factorinterpretation circuit is structured to interpret one or more schedulingfactors. The one or more agglomerate network circuits are eachstructured to generate a corresponding schedule. The one or moreconnector circuits are each structured to pass at least one of theschedules as inputs to at least one of the one or more agglomeratenetwork circuits. The schedule selector circuit is structured to selectat least one of the schedules. The schedule provisioning circuit isstructured to transmit the selected schedule.

Another example embodiment of the present disclosure, utilizing one ormore aspects as set forth preceding, includes a method for configuring ascheduling system. The method includes generating a plurality ofschedules for a plurality of targets using different configurations ofan agglomerate network, determining a performance score of the pluralityof schedules, and identifying configurations of the agglomerate networkand targets with schedules above a performance score/threshold. Themethod further includes receiving a request for a schedule for a target,configuring the agglomerate network for the target based on theidentified configurations, and generating the schedule using theconfigured agglomerate network. In certain embodiments, tracking theperformance includes tracking changes made to the schedules. In certainembodiments, configuring the agglomerate network incudes selectingscheduling models. In certain embodiments, configuring the agglomeratenetwork includes selecting forecasting models. In certain embodiments,configuring the agglomerate network includes configuring data biases ofdata sources.

An example agglomerated network use case includes a franchise workschedule scenario and/or situation. For example, an embodiment of theAutonomous Agglomerated Resource Utilization Modeler, as describedherein, may utilize correlated agglomerated models to produce improvedshift work schedules that balance both implicit and explicit qualityparameters. Such embodiments may optimize quality parameters across oneor more hierarchical agglomerated scheduling models, and one or moresecondary, iterative agglomerated models. In this example, the systemmay generate shift schedules for a chain of restaurants operating inNorthern Michigan during the winter. The franchise operator may run achain of six restaurants, one of which is new. The franchise owner maywant to develop a shift schedule for the operator's six franchises inthe early fall for Thanksgiving week.

Other use cases include optimizing a schedule for one or more of: 1)benefits to education, e.g., experienced employee with several newemployees, or maximize cross-training across skills/departments; 2)benefits to environment, e.g., maximize at least one of ridesharing,bike/walk to work, public transportation, etc., and/or maximize thepercent of resources (used by those scheduled tasks/people) that arerenewable; 3) benefits to utilities bills, e.g., certain jobs/skills mayuse more resources (electric/gas) and can be scheduled for when thoserates are predicated to be lower, and/or to maximize the percent ofresources that are renewable; 4) schedule to maximize throughput, e.g.,right before an estimated peak demand it may be optimal to have the mostefficient employees there; and/or 5) amount of system resources to beused generating schedules, e.g., if need extra resources, e.g., virtualmachines, are needed to generate a particular schedule, it may beoptimal to generate the schedule at off-peak times and/or during betterrates.

Embodiments may include agglomerated input handler processing ofexternal data sources. In addition to extracting model input data fromunformatted or formatted sources, the Agglomerated Input Handler mayassess the quality and reliability of its input sources. For example,the system may take in weather information from a variety of sourcesthat possess different characteristics and quality ratings. Initially,the reliability of these data sources may default to a set level basedon user settings or by drawing on data developed from cross-domainexperience with the same or a similar model.

Embodiments may include learning external data sourcequality/reliability. In an unsupervised manner, the system maycontinually update an input quality model based on actual versuspredicted results. If a given external data source provides predictivedata (e.g., a weather forecast), the system learns to what degree a newinput is reliable based on the actual weather once the predictivetimeframe is reached. In certain embodiments, the system may alsoutilize direct feedback (labeling) from user(s) as a supervised learninginput.

A use case input example is provided below. In this franchise workschedule example, the system may receive an updated weather report whichpredicts six (6) inches of snow Thanksgiving night, prior to an early,black-Friday opening. When the original schedule for the current weekwas adopted, clear skies may have been anticipated. As such, theAgglomerated Input Handler may monitor weather reports and processes anyupdated data. Based on its learning concerning the accuracy of thesource (the National Weather Service), the advance forecast time (18hours), and the range of snowfall predicted (anticipated snowfall, six(6) inches: range of anticipated snowfall, four (4) to twelve (12)inches, and the prior snowfall prediction (no snow).

Embodiments may include an updated agglomerated weather model.Continuing with the franchise work schedule example, the AgglomeratedInput Handler may update a probabilistic agglomerated weather modelbased on the received NWS update. Based on its learned experience, thesystem may provide the following probability distribution for theupcoming snowfall:

-   -   No snow/dusting: 5%    -   2 inches: 5%    -   4 inches: 20%    -   6 inches: 40%    -   8 inches: 15%    -   10 inches: 10%    -   12 inches: 5%

Embodiments may include triggering the generation of a new or updatedagglomerate model. Upon the receipt of a new or updated external input,or the generation of a new or updated agglomerated model, or theactivation of one or more time-based trigger(s), the AutonomousEvolution Controller may direct the creation or update one oragglomerated models. Alternatively, the Autonomous Evolution Controllermay determine that the generation of new or updated agglomerated modelsis not required at this time, e.g., if the new or updated input or modeldata is not evaluated to be significant at this time.

The Autonomous Evolution Controller may utilize historical data and/oruser inputs, to learn whether a given new or updated external input, ora new or updated agglomerated model should trigger the creation orupdate one or more primary scheduling models. In an embodiment, whereprior historical or comparable information is not available, the systemmay preferentially update potentially affected agglomerate models untiladequate data has been collected to determine if the new data needs tobe propagated forward.

The autonomous evolution controller may make independent propagationdecisions for one or more agglomerate models which utilize the new orupdated data as an input. In the example above, the weather data may beconsumed by one or more agglomerate models such as a school closing,general business closing model, commute model, absenteeism model, andfranchise closing model. While the absenteeism model may consume theoutputs of the agglomerated weather model, it may also consume outputsfrom the school closing model, general business closing model, and/orcommute model.

Embodiments may include learning evolution triggers. Initially thesystem may use default alert levels, quality measures, time limits,processing limits, trigger values from similar businesses, other means,or a combination of the above, to determine what agglomerate models, ifany, need to be updated based on the receipt of a new or updated input,model, or alert. However, as processing time permits, the system maypreferentially update the agglomerate models. In embodiments, the system“learns” if such an update needed to be run based on comparing theresults of the existing agglomerate models and any newly generatedagglomerate models. If the change did not propagate through to thegeneration of a new schedule, i.e., the revised input did not ultimatelyresult in a revised schedule or other output result, the evolutioncontroller learns about when and under what conditions updating a givenagglomerate model may not be necessary.

If the update is propagated through to a user, the autonomous evolutioncontroller learns if the updated output was significant based on whethera user accepts or rejects any suggested change. Additionally, the systemmay adjust the perceived quality of the output depending on how well thenewly modeled schedule is adhered to by the franchise's employees.

For example, if an updated weather forecast calls for six (6) inches ofsnow versus one (1) inch of snow in the hours leading up to a shiftstart, the system may rerun an agglomerate absenteeism model that isglobally trained (where globally refers to trained over a large set ofsimilar businesses) to assess general staff availability. If the globalabsenteeism model indicates that the probability of key-staff missing ashift is high, the system may preferentially trigger the running of alocal absenteeism model which uses the global absenteeism output as afeature input and reassesses the likelihood of individual staffabsenteeism.

Embodiments of the current disclosure provide for feedback aspects, suchas employee feedback, to be used/considered in adjusting how a scheduleis generated. Accordingly, certain aspects of the current disclosureprovide for may provide for a responsive scheduler, e.g., a schedulerthat uses employee feedback to adjust how an AI, as disclosed herein,generates a schedule. The responsive scheduler may form part ofcomponent module 114 (FIG. 1 ) and, as such, the feedback may becollected via computer surveys, e.g., 140 (FIG. 1 ) and/or other means,e.g., extraction of trends 142 (FIG. 1 ) as disclosed elsewhere herein,such as trends extracted by the marketplace components 112 (FIG. 1 ).For example, in embodiments, initially the model of individual staffabsenteeism may be drawn from a default model, e.g., user entered data,and/or a combination of the above where a new employee is matched to anexisting profile based on similar employee profile characteristics,responses to questionnaires, transportation type, family composition,and distance to the job site. Feedback, e.g., survey responses, on thegenerated schedule(s) may then be provided to the scheduler so that itcan learn over time and use its own historic performance to weigh andtune the various inputs and produce a more optimal output. Embodimentsof the responsive scheduler, however, may seek to balance the value ofreceiving employee feedback with over surveying, e.g., hassling,employees.

In embodiments, an artificial intelligence (AI) system, tasked withgenerating a schedule, may be influenced via employee feedback on aprior schedule. In embodiments, the influence may be directly to the AIsystem, e.g., the influence may be a change to weights in a neuralnetwork and/or a change to a connector that adjusts inputs and/oroutputs to one or more agglomerate network circuits in an agglomeratenetwork, as disclosed herein. In other words, embodiments of the currentdisclosure provide for the generation of schedule data that isresponsive to employee feedback.

In embodiments, the feedback may be collected via computer surveysand/or other means, e.g., inferred from activity in a schedulermarketplace, wherein trends are extracted from the feedback. Feedbackmay be gathered via online surveys, traditional paper surveys, anonymoussubmissions, focus groups, and the like. Embodiments of the currentdisclosure may use historic performance to weigh and/or tune variousinputs to feedback to produce a more optimal output. Embodiments maybalance extracted trends with the needs of the organization, whereorganizational needs and trends may be given a common score to determinewhich dominates. Trends associated with a high number of employees maybe given a high score and organizational needs may be given ratings bymanagers. In embodiments, trends may be determined based on a number ofsimilar responses from different employees.

Embodiments of a responsive scheduler may be a module/model/circuit thatreceives inputs, e.g., a schedule and/or other data, e.g., biases, as:direct input, e.g., the responsive scheduler acts as a standalonemodule; as direct input to an agglomerate network, e.g., without use ofconnectors; from connectors, e.g., the responsive scheduler is one of aplurality of modules/models/circuits within an agglomerate network; andthe like. For example, the responsive scheduler may be amodule/model/circuit within an agglomerate network, as disclosed herein,that evaluates a schedule, generated by human and/or a schedulercircuit, against trends extracted from employee feedback and generatesan output, e.g., a bias. The responsive scheduler circuit may, in turn,feed the output back into the scheduler circuit (or back to the human asa message) to form a feedback loop which tries to reach equilibriumand/or optimization of various biases in the agglomerate network. Theconnections between the responsive scheduler circuit and the variousmodules/models/circuits of the agglomerate network may be accomplishedvia connectors.

Accordingly, FIG. 59 depicts an apparatus 110100 for responsivescheduling. The apparatus 110100 includes a schedule interpretationcircuit 110102 structured to interpret schedule data 110110; a feedbackinterpretation circuit 110104 structured to interpret feedback data110112 corresponding to the schedule data 110110; a feedback influencercircuit 110106 structured to generate, based at least in part on thefeedback data, a feedback influence command value 110114 structured toeffect a change of a property 110116 of the schedule data; and afeedback influencer provisioning circuit 110108 structured to transmit110118 the feedback influence command value. The change to the property110116 of the schedule data may be a direct change, e.g., directmanipulation of the schedule data, and/or accomplished via adjusting aconnector that in turn results in a change to the schedule data. Forexample, an agglomerate network may have multiple scheduler circuitsthat compete to be the first to generate a schedule that optimizes oneor more biases, as disclosed herein. Adjusting a connector may result inthe output of a particular circuit and/or model being down-weighted whenused as input to a scheduler circuit. For example, the output of aprofitability circuit tasked with influencing a scheduler circuit tooptimize for profitability may be down-weighted via a connector bias ifthe feedback shows employees are unhappy with past schedules that weregenerated when the profitability circuit had more influence on thescheduler circuit.

Referring to FIG. 60 , certain further aspects of the apparatus 110100are described following, any one or more of which may be present incertain embodiments. For example, in embodiments, the feedbackinfluencer circuit 110108 generates the feedback influence command valuebased at least in part on machine learning 110202. The machine learning110202 may extract trends 110204 from the feedback data, and the trendsare used to generate the feedback influence command value. Inembodiments, the machine learning 110202 may be a neural network trainedover a labeled training set that includes historical feedback data andassociated trends.

In embodiments, the apparatus 110100 further includes a transparencycircuit 220205 structured to generate an electronic communication thatindicates the feedback data influenced the schedule data, and atransparency provisioning circuit 220207 structured to transmit theelectronic communication. As will be appreciated, the electroniccommunication may convey to an employee that their feedback was receivedand considered by their employer when generating the schedule data110110. In embodiments, the electronic communication may include avisual and/or audio message. For example, the electronic communicationmay be a textual description that conveys one or more extracted tends110204, and may further include a textual description of changes to theschedule data 110110 that were made based on the feedback. For example,an extracted trend 110204 may indicate that employees assigned to theschedule strongly disfavor shifts exceeding twelve hours, that thistrend 110204 was considered in generating the schedule data 110110, andthat the result was that the schedule data 110110 corresponds to aschedule that does not have any shifts exceeding twelve (12) hours butdoes have an additional shift. In embodiments, the electroniccommunication may indicate a version of a schedule that was generatedprior to incorporation of feedback along with a version of the schedulethat has been adjusted and/or regenerated based at least in part on thefeedback. As will be appreciated, providing transparency to employeeswith respect to the schedule generation process and the incorporation offeedback, as disclosed herein, improves the relationship betweenemployees and their employers which, in turn, may improve employeeproductivity and/or reduce turnover.

In embodiments, the trends may include a common favorite shift or acommon unfavorite shift 110206. In embodiments, trends may include apreferred amount of hours worked per day, week, month, year, etc. Forexample, the machine learning 110202 may determine a trend 110204 thatmarried workers between the ages of twenty-five to thirty years oldgenerally disfavor shifts starting after five pm, and structure thefeedback influence command value to mitigate the likelihood of suchworkers being assigned to shifts starting after five pm. In embodiments,the machine learning 110202 may detect a trend that a worker of aparticular demographic may prefer shifts prior to a religious holiday,presumably to earn extra money for the holiday. In embodiments, trendsmay include a commonly preferred co-worker. In such embodiments, thechange to the schedule data may include increasing the number ofemployees who work with the commonly preferred co-worker over a givenperiod of time. For example, a highly favorite employee (as indicated bythe feedback) may be given shorter but more numerous shifts so that theycan be scheduled to work with a larger number of different employees. Inembodiments, the highly favorite employee may be rotated throughdifferent shifts while other employees are assigned non-rotating shifts.In embodiments, the highly favorite employee may be assignednon-rotating shifts while other employees are rotated through the highlyfavorite employee's shifts.

In embodiments, the feedback influencer circuit generates the feedbackinfluence command value by balancing the extracted trends with needs ofan organization 110208. For example, extracted trends 110204 and a needof an organization may be assigned values/scored on a common scale,e.g., a range of one (1) to one-hundred (100) for purposes of comparingand/or weighting. In such embodiments, an extracted trend may be given ahigh value, e.g., ‘80’, if the trend 110204 is supported by a largenumber of employees, e.g., 50% or more of the workforce. The need of theorganization may be assigned a value via user input and/or via anartificial intelligence. For example, a shift during a holiday seasonfor a retailer may be assigned a high value, e.g., ‘90’, if the retailergenerates a significant amount of annual profits during the holidayseason, as opposed to a non-holiday season shift which may be assigned alow value, e.g., ‘20’. Non-limiting examples of needs of an organization110208 include: urgent shifts, a minimum number of open-store hours, aminimum number of expected sales; a minimum amount of expected profit, amaximum amount of operating costs, and the like.

In embodiments, the feedback data corresponds to employees 110210 thatoperated under a schedule defined, in part, by the schedule data. Assuch, the feedback data may be a direct reflection of the scheduledata's favorability with employees. In embodiments, the feedback datacorresponds to employees 110210 that did not work under the scheduledata. Accordingly, the feedback data may be an indirect reflection ofthe schedule data's favorability with employees. For example, thefeedback data in such embodiments may indicate an industry wide trend,e.g., employees generally disfavor working Sunday morning shifts.

In embodiments, the feedback influence command value may be structuredto generate a message to a user 110212. In such embodiments, the usermay be an individual tasked with generating a schedule for an entity andthe message may help guide the user in designing the schedule data,either by directly editing the schedule data and/or making an adjustmentto a connector. The message may include a text-based message, a visualindicator, and/or an audio-based message. For example, the message maystate that an extracted trend was found and indicates that employees arewilling to work an additional three hours per shift for four weeksin-a-row if they receive an extra week of vacation time.

In embodiments, the feedback influence command value may be structuredto adjust a bias of a connector in an agglomerate network 110214, asdisclosed herein, and/or to directly adjust the schedule data 110216, asalso disclosed herein. In embodiments, the schedule data corresponds toa portion of a schedule 110218 that is less than the entire schedule.For example, the feedback influence command value may be structured toadjust a single shift in a schedule. In embodiments, the feedbackinfluence command value may be structured to adjust a subset of a totalnumber of shifts in a schedule. In embodiments, the feedback maycorrespond to a single shift and/or a series of shifts in a schedule. Inother words, embodiments of the current disclosure provide for generalfeedback for a schedule, as a whole, and/or for more tailored feedbackthat identifies particular portions of a schedule.

For example, in embodiments, each of a plurality of distinct portions ofthe feedback data may be mapped 110220 to a corresponding distinctportion of the schedule data. As such, a distinct portion of thefeedback data may include an identifier for one or more shifts. As willbe appreciated, mapping distinct portions of the feedback data tocorresponding distinct portions of the schedule data may provide formore accurate adjustments to the schedule data which may be better ableto accommodate balancing employer needs vs employee needs. Inembodiments, each of the plurality of distinct portions of the feedbackdata may represent a score value 110222, e.g., a score between one (1)and one-hundred (100) where one (1) corresponds to least favorable valueand one-hundred (100) corresponds to most favorable value.

Referring to FIG. 61 , a method 110300 for responsive scheduling, inaccordance with embodiments of the current disclosure, is shown. Themethod 110300 may be performed via apparatus 110100 and/or any othercomputing device disclosed herein. The method 110300 includesinterpreting 110302, via a schedule interpretation circuit, scheduledata; interpreting 110304, via a feedback interpretation circuit,feedback data corresponding to the schedule data; generating 110306, viaa feedback influencer circuit and based at least in part on the feedbackdata, a feedback influence command value structured to effect a changeof a property of the schedule data; and transmitting 110308, via afeedback influencer provisioning circuit the feedback influence commandvalue.

Referring to FIG. 62 , certain further aspects of the method 110300 aredescribed following, any one or more of which may be present in certainembodiments. For example, the feedback influencer circuit generates thefeedback influence command value 1100114 based at least in part onmachine learning 110402. The machine learning extracts trends 110404from the feedback data 110112, and the trends are used to generate thefeedback influence command value. The trends may include a commonfavorite shift or a common unfavorite shift 110406. The feedbackinfluencer circuit generates the feedback influence command value bybalancing the extracted trends with needs of an organization 110408. Thefeedback data corresponds to employees 110410 that operated under aschedule defined, in part, by schedule data 110430. The feedbackinfluence command value is structured to generate a message to a user110412. The feedback influence command value is structured to adjust abias of a connector in an agglomerate network 110414. The feedbackinfluence command value is structured to directly adjust the scheduledata 110416. The schedule data corresponds to a portion of a schedule110418 that is less than the entire schedule. Each of a plurality ofdistinct portions of the feedback data are mapped 110420 to acorresponding distinct portion of the schedule data. Each of theplurality of distinct portions of the feedback data represent a scorevalue 110422.

FIG. 63 depicts an agglomerate network 110500 for generating scheduledata, in accordance with embodiments of the current disclosure, thatprovides for responsive scheduling. The agglomerate network 110500includes a scheduler circuit 110502 structured to output the scheduledata 110514; and a connector circuit 110504 structured to adjust atleast one of an input to the scheduler circuit and/or the schedule dataoutputted by the scheduler circuit. The agglomerate network 110500further includes a responsive scheduler circuit 110506 structured to:interpret 110508 the schedule data; generate 110510, based at least inpart on feedback data 110516, a feedback influence command value 110518structured to trigger an adjustment to a connector 110520. Theadjustment is structured to effect a change of at least one of the inputto the scheduler circuit or the schedule data outputted by the schedulercircuit. The responsive scheduler circuit 110506 is further structuredto transmit 110512 the feedback influence command value 110114.

Referring to FIG. 64 , certain further aspects of the agglomeratenetwork 110500 for generating schedule data are described following, anyone or more of which may be present in certain embodiments. As shown,the responsive scheduler circuit 110506 may generate the feedbackinfluence command value 110114 based at least in part on machinelearning 110602. In embodiments, the machine learning extracts trends110604 from the feedback data 110112, and the trends may be used togenerate the feedback influence command value 110114. The trends mayinclude one or more of a common favorite shift or a common unfavoriteshift 110606.

Referring to FIG. 65 , a non-transitory computer-readable medium 110700in accordance with embodiments of the current disclosure is shown. Thenon-transitory computer-readable medium 110700 stores instructions thatadapt at least one processor to: interpret schedule data 110702;interpret feedback data 110704 corresponding to the schedule data;generate 110706, based at least in part on the feedback data, a feedbackinfluence command value 110714. The responsive scheduler circuit 110506may be structured to effect a change of a property of the schedule data,as disclosed herein. The stored instructions further adapt the at leastone processor to transmit the feedback influence command value 110708.

Referring to FIG. 66 , certain further aspects of the non-transitorycomputer-readable medium 110700 are described following, any one or moreof which may be present in certain embodiments. For example, inembodiments, the feedback influence command value 110114 may bestructured to generate a message to a user 110812. In embodiments, thefeedback influence command value 110114 may be structured to adjust abias of a connector, as disclosed herein, in an agglomerate network110814. The feedback influence command value 110114 may be structured todirectly adjust the schedule data 110816, as disclosed herein.

FIG. 67 illustrates a method 110900 for responsive scheduling inaccordance with embodiments of the current disclosure. The method 110900includes transmitting 110902, via a local computing device, feedbackdata to a scheduling platform hosted on one or more remote servers. Themethod 110900 further includes accessing 110904, via the local computingdevice, schedule data generated via the scheduling platform based atleast in part on a responsive scheduler circuit. The method 110900further executing 110906 a schedule based at least in part on theschedule data; wherein the responsive scheduler circuit is structured toinfluence 110908 the schedule data based at least in part on thefeedback data.

Referring to FIG. 68 , certain further aspects of the method 110900 aredescribed following, any one or more of which may be present in certainembodiments. For example, the schedule data corresponds to a portion ofa schedule 111018 that is less than the entire schedule.

Over-time, and across companies where personal privacy selections andlegal agreements allow, the system may train an atomic absenteeism modelto an individual employee. When compared to predictions based on similarshift conditions & employee characteristics, their actual on-timearrival, absenteeism rate is used to retrospectively train the model.

Embodiments of the system may also provide for schedule mimicking, e.g.,generation of a number of different schedules that are similar to humangenerated schedules and/or the copying and/or imitation of portions of afirst schedule when generating and/or adjusting a second schedule.Schedule mimicking may be performed as part of modules/components 126and 128 (FIG. 1 ). Received selections from the generated schedules maybe used as feedback for algorithm refinement. Such mimicking may also bewith respect to portions of a schedule. Mimicking may be based on userfeedback, e.g., some employees like aspects of a particular schedulewhile other employees like aspects of another schedule. As such,embodiments of the system, as described herein, may mimic sub portion(s)of one or more schedules that different people like. Embodiments of thesystem, as described herein, may also mimic liked aggregations orimplications from schedules (hours or average or standard deviation (SD)of seniority (small SD for peak demand, high SD for max educationetc.)). As will be understood, a manager may gain trust and/oreventually come to rely on embodiments of the system, as describedherein, or the manager is able to see that the system is producingschedules similar to and/or improved over ones previously produced bythe manager.

In embodiments, computer generated schedules, as disclosed herein, maybe purposefully designed/generated to be similar to human generatedschedules and/or portions thereof. In other words, embodiments of thecurrent disclosure provide for computerized apparatuses and methods thatmimic schedules created manually by humans, e.g., department managers.As will be appreciated, mimicking the style and/or preferences of ahuman by a computerized apparatus and/or method, as disclosed herein,may provide for such an apparatus and/or method to generate a schedulethat accounts for information that may be undocumented, e.g., notrecorded in a database and/or other computerized storage system, and/orgenerally unknown outside of the human that generated the schedule. Inother words, embodiments of the current disclosure provide forapparatuses and methods that are better able to account for undocumentedinformation with respect to generating a schedule for an entity, ascompared to systems and/or approaches that may only consider documentedinformation.

As will be understood, the generated schedules may be used as feedbackfor further schedule generation and refinement. Embodiments may providefor a human manager to see that the computerized system is producingschedules comparable to their own, which may help to improve themanager's trust in the system. Embodiments may mimic different scheduleportions that have been liked by different employees in the sameschedule. For example, embodiments may mimic “liked” aggregations,aspects and/or implications from schedules, e.g., hours, average orstandard deviation (SD) of seniority (small SD for peak demand, high SDfor max education, etc.). As such, embodiments of the current disclosuremay be incorporated with aspects of a schedule spreader and/or aresponsive scheduler, as disclosed herein.

Embodiments of schedule mimicking may be a module/circuit/model thatreceives inputs, e.g., a schedule and/or other data, e.g., biases, as:direct input, i.e., the schedule mimicking module/circuit/model acts asa standalone module/circuit/model; as direct input to an agglomeratenetwork, i.e., without use of connectors; and/or from connectors, i.e.,the schedule mimicking module is one of a plurality ofmodules/circuits/models within an agglomerate network. For example,schedule mimicking may take the form of a schedule generationmodule/circuit/model within an agglomerate network that passes itsoutput (schedule data) to other modules in the agglomerate network forevaluation where the other modules generate output(s), e.g., a bias. Theother modules may, in turn, feed the output back into the schedulemimicking module/circuit/model to form a feedback loop which tries toreach equilibrium and/or optimization of various biases in theagglomerate network while keeping the generated schedules comparable toones generated by managers. The connections between the schedulemimicking module and the various other modules of the agglomeratenetwork may be accomplished via connectors.

Accordingly, FIG. 69 illustrates an apparatus 130100 for schedulemimicking, in accordance with embodiments of the current disclosure. Theapparatus 130100 includes a historic schedule interpretation circuit130102 structured to interpret historical schedule data 130118corresponding to a schedule 130116 designed, in part, by an entity130114, e.g., an employee, a manager, a department, an organization, andthe like. The apparatus 130100 further includes a mimicking circuit130104 structured to: extract 130108 a schedule trend 130122 from thehistorical schedule data; identify 130110 a portion 130120 of thehistoric schedule data corresponding to the extracted schedule trend;and generate 130112 schedule data 130124 based at least in part on theidentified portion. The apparatus 130100 further includes a scheduledata provisioning circuit 130106 structured to transmit 130126 theschedule data. Non-limiting examples of historical schedule data 130118include digital representations of schedules, e.g., such as electroniccalendar file types, e.g., “.ics”, “.ifb”, and/or the like. Furthernon-limiting examples of historical schedule data 130118 may relate to aschedule, a total number of shifts that must be filled, an entity, e.g.,a corporation and/or department for which the schedule is for, and thelike. The portion 130120 of the historic schedule data may correspond toa shift, a group of shifts, a day, week, month, year, etc., a subset ofavailable employees, one or more locations associated with the schedule,etc. Non-limiting examples of schedule trends 130122 include: an averageshift length; an average shift starting time; and average shift endtime; an average number of workers per shift; one or more groupings ofemployees across one or more shifts; an average time between shifts foran employee; and/or the like. The portion of the schedule data beingmimicked may be less than the entire schedule data.

Referring to FIG. 70 , certain further aspects of the apparatus 130100are described following, any one or more of which may be present incertain embodiments. For example, the entity 130114 may be a manager130202 of a business unit 130204, 130206 to which the schedule datacorresponds. Non-limiting examples of a business unit 130204, 130206include: teams, departments, divisions, etc. In embodiments, the entity130114 may be a manager 130202 of a first business unit 130204 distinctfrom a second business unit 130206, and the schedule data 130124corresponds to the second business unit 130206. As will be understood,such embodiments provide for successful schedules to be copied/sharedacross business units in an organization. In embodiments, the firstbusiness unit 130204 and the second business unit 130206 are in a sameorganization. In embodiments, the first business unit 130204 and thesecond business unit 130206 are in distinct organizations. As will beappreciated, such embodiments provide for successful schedules to becopied/shared across different businesses.

The extraction of the schedule trend 130122 may be based at least inpart on machine learning 130208, which may involve a neural network130210 trained to match a portion of the schedule data with an event130212. In such embodiments, generation of the schedule data may bebased at least in part on an association of the matched portion andschedule trend. Non-limiting examples of events 130212 include: aholiday 130214, inclement weather, an equipment failure 130216, ashortage 130218 of materials or product for sale, and/or the like. Aswill be appreciated, such embodiments provide for mimicking of amanager's scheduling style for a holiday season and/or during austereevents, as disclosed herein.

In embodiments, the mimic command value 130220 is structured to generatea message that conveys how to structure a portion of a schedule.Non-limiting examples of the message include a textual description, avisual depiction of a schedule, and/or audio instructions. The messagemay identify which portions of a prior schedule are being mimickedand/or a supporting reason for the mimicking, e.g., workers providedpositive feedback on the portion being mimicked and/or positive feedbackon a manager whose scheduling style is being mimicked.

In embodiments, the mimic command value is structured to adjust aconnector circuit of an agglomerate network 130222, as disclosed herein.The adjustment to the connector circuit can be structured to set orchange a bias 130224 of the connector circuit. For example, the bias130224 may control a weighing that affects how much contribution a priorschedule (being mimicked) will contribute to a schedule generated by ascheduler circuit, as disclosed herein.

In embodiments, the apparatus 130100 further interprets secondhistorical schedule data and mimics portions of both the first andsecond historical schedule data. In other words, embodiments of theapparatus 130100 may mimic different portions of different schedules.

In embodiments, the apparatus 130100 further includes a feedbackinterpretation circuit 130226 structured to interpret feedback data130228 on the schedule data, e.g., embodiments of the apparatus 130100may incorporate aspects of a responsive scheduler, e.g., apparatus110100 (FIG. 59 ), as disclosed herein.

Non-limiting examples of feedback data 130228 include employee surveyson the schedule, “likes” or “dislikes” provided via a social media(public or private) platform, and/or the like. In such embodiments, theextracted schedule trend may be based at least in part on the feedbackdata. For example, the extracted trend may be a commonly liked portionof the historic schedule data. The commonly liked portion of thehistoric schedule data may correspond to an aggregation or animplication of the schedule data, e.g.: a total number of hours workedand/or pay per shift, day, week, month, year, etc.; a total amount ofvacation time; a minimum shift staffing number, and/or the like. Inembodiments, the aggregation relates to at least one of hours worked, anaverage deviation, or a standard deviation. In embodiments, theextracted trend may be a highly rated portion of the historic scheduledata. The aggregation may further relate to seniority state and/or a payvalue.

Referring to FIG. 71 , a method 130300 for schedule mimicking, inaccordance with embodiments of the current disclosure, is provided. Themethod 130300 may be performed via apparatus 130100 and/or any othercomputing device disclosed herein. The method 130300 includesinterpreting, via a historic schedule interpretation circuit, historicalschedule data corresponding to a schedule designed, in part, by anentity 130302; extracting, via a mimicking circuit, a schedule trendfrom the historical schedule data 130304; identifying, via the mimickingcircuit, a portion of the historical schedule data corresponding to theextracted schedule trend 130306; generating, via the mimicking circuit,schedule data based at least in part on the identified portion 130308;and transmitting, via a schedule data provisioning circuit, the scheduledata 130310.

Turning to FIG. 72 , certain further aspects of the method 130300 aredescribed following, any one or more of which may be present in certainembodiments. For example, the entity 130402 may be a manager 130404 of abusiness unit 130406, 130408 to which the schedule data corresponds. Theentity 130402 may be a manager of a first business unit 130406 distinctfrom a second business unit 130408, and the schedule data corresponds tothe second business unit 130408. The first business unit 130406 and thesecond business unit 130408 may be in a same organization. The firstbusiness unit 130406 and the second business unit 130408 may be indistinct organizations. In embodiments, extraction of the schedule trend130410 is based at least in part on machine learning 130412. The machinelearning may involve a neural network 130414 trained to match a portionof the schedule data with an event 130416. In such embodiments,generation of the schedule data is based at least in part on anassociation of the matched portion and schedule trend. As discussedherein, non-limiting examples of events include: a holiday 130418,inclement weather, an equipment failure 130420, a shortage 130422 ofmaterials or product for sale, and/or the like.

In embodiments, generating, via the mimicking circuit, schedule databased at least in part on the identified portion 130308 may includegenerating a mimic command value 130424 structured to generate a messagethat conveys how to structure a portion of a schedule. In embodiments,the mimic command value is structured to adjust a connector circuit ofan agglomerate network 130426, where the adjustment is structured to setor change a bias 130428 of the connector circuit. In embodiments, theportion of the schedule data is less than the entire schedule data. Themethod 130300 may further include interpreting second historicalschedule data and mimicking portions of both historical schedule data.The method 130300 may further include interpreting, via a feedbackinterpretation circuit, feedback data 130430 on the schedule data,wherein the extracted schedule trend is based at least in part on thefeedback data. As discussed herein, the extracted trend may be acommonly liked portion of the schedule data. The commonly liked portionof the schedule data may correspond to an aggregation or an implicationof the schedule data. In embodiments, the aggregation may relate to atleast one of hours worked, an average deviation, or a standarddeviation. The extracted trend may be a highly rated portion of theschedule data. In embodiments, the aggregation further relates toseniority state or a pay value.

FIG. 73 depicts another apparatus 130500 for schedule mimicking, inaccordance with embodiments of the current disclosure. The apparatus130500 includes a historic schedule interpretation circuit 130502structured to interpret historical schedule data 130518 corresponding toa schedule 130516 designed, in part, by an entity 130514. The apparatus130500 further includes a mimicking circuit 130504 structured to:extract 130508 a schedule trend 130522 from the historical scheduledata; identify 130510 a portion 130520 of the historical schedule datacorresponding to the extracted schedule trend; and generate 130512 amimic command value 130526 based at least in part on the identifiedportion. The mimic command value is structured to trigger an adjustmentto schedule data 130524 generated by a scheduler circuit 130505. Theapparatus 130500 further includes a mimic command provisioning circuit130506 structured to transmit 130528 the mimic command value.

Referring to FIG. 74 , certain further aspects of the apparatus 130500are described following, any one or more of which may be present incertain embodiments. For example, the mimic command value 130526 may bestructured to generate a message that conveys how to structure a portionof a schedule, as disclosed herein. In embodiments, the mimic commandvalue may be structured to adjust a connector circuit of an agglomeratenetwork 130622, as further disclosed herein. The adjustment to theconnector circuit may be structured to set or change a bias 130624 ofthe connector circuit.

Referring to FIG. 75 , a method 130700 for schedule mimicking, inaccordance with embodiments of the current disclosure, is provided. Themethod 130700 may be performed via apparatus 130500 and/or any othercomputing device disclosed herein. The method 130700 includesinterpreting 130702, via a historic schedule interpretation circuit,historical schedule data corresponding to a schedule designed, in part,by an entity. The method 130700 further includes extracting 130704, viaa mimicking circuit, a schedule trend from the historical schedule data.The method 130700 further identifying 130706, via the mimicking circuit,a portion of the historical schedule data corresponding to the extractedschedule trend. The method 130700 further includes generating 130708,via the mimicking circuit, a mimic command value based at least in parton the identified portion, wherein the mimic command value is structuredto trigger an adjustment to schedule data generated by a schedulercircuit. The method 130700 further includes transmitting 130710, via amimic command provisioning circuit, the mimic command value.

Referring to FIG. 76 , certain further aspects of the method 130700 aredescribed following, any one or more of which may be present in certainembodiments. For example, the mimic command value 130824 can bestructured to generate a message that conveys how to structure a portionof a schedule. The mimic command value can be structured to adjust aconnector circuit of an agglomerate network 130826. The adjustment tothe connector circuit may be structured to set or change a bias 130828of the connector circuit, as further disclosed herein.

Referring to FIG. 77 , an agglomerate network 130900 for generatingschedule data 130918 via schedule mimicking, in accordance withembodiments of the current disclosure, is shown. The agglomerate network130900 includes a scheduler circuit 130902 structured to output theschedule data 130918; a connector circuit 130904 structured to adjust atleast one of an input to the scheduler circuit or the schedule dataoutputted by the scheduler circuit; and a schedule mimicker circuit130906. The schedule mimicker circuit 130906 is structured to: interpret130908 historical schedule data 130924; extract 130910 a schedule trend130922 from the historical schedule data; identify 130912 a portion130920 of the schedule data corresponding to the extracted scheduletrend; and generate 130914 a mimic command value 130926 based at leastin part on the identified portion. The mimic command value is structuredto trigger an adjustment to the connector circuit, wherein theadjustment is structured to effect a change of at least one of the inputto the scheduler circuit or the schedule data outputted by the schedulercircuit. The schedule mimicker circuit 130906 is further structured totransmit 130916 the mimic command value.

Referring to FIG. 78 , certain further aspects of the agglomeratenetwork 130900 are described following, any one or more of which may bepresent in certain embodiments. Accordingly, the mimic command value130926 may be structured to generate a message that conveys how tostructure a portion of a schedule. In embodiments, the mimic commandvalue may be structured to adjust a connector circuit of an agglomeratenetwork 131022. As such, the adjustment to the connector circuit can bestructured to set or change a bias 131024 of the connector circuit.

Referring to FIG. 79 , a non-transitory computer-readable medium 131100may be provided. The non-transitory computer-readable medium 131100stores instructions that adapt at least one processor to: interpret131102 historical schedule data corresponding to a schedule designed, inpart, by an entity; extract 131104 a schedule trend from the historicalschedule data; identify 131106 a portion of the historical schedule datacorresponding to the extracted schedule trend; generate 131108 scheduledata based at least in part on the identified portion; and transmit131110 the schedule data.

Referring to FIG. 80 , certain further aspects of the non-transitorycomputer-readable medium 131100 are described following, any one or moreof which may be present in certain embodiments. For example, inembodiments, the extraction of the schedule trend 131210 is based atleast in part on machine learning 131212 that may involve a neuralnetwork 131214 trained to match a portion 131218 of the schedule datawith an event 131216. In such embodiments, generation of the scheduledata may be based at least in part on an association of the matchedportion and schedule trend.

Referring to FIG. 81 , another non-transitory computer-readable medium131300, in accordance with embodiments of the current disclosure, isshown. The non-transitory computer-readable medium 131300 storesinstructions that adapt at least one processor to: interpret 131302historical schedule data corresponding to a schedule designed, in part,by an entity; extract 131304 a schedule trend from the historicalschedule data; identify 131306 a portion of the historical schedule datacorresponding to the extracted schedule trend; and generate 131308 amimic command value based at least in part on the identified portion.The mimic command value is structured to trigger an adjustment toschedule data generated by a scheduler circuit. The instructions furtheradapt the at least one processor to transmit 131310 the mimic commandvalue.

Referring to FIG. 82 , certain further aspects of the non-transitorycomputer-readable medium 131300 are described following, any one or moreof which may be present in certain embodiments. For example, the mimiccommand value 131424 may be structured to generate a message thatconveys how to structure a portion of a schedule. In embodiments, themimic command value may be structured to adjust a connector circuit ofan agglomerate network 131426. The adjustment to the connector circuitmay be structured to set or change a bias 131428 of the connectorcircuit.

The systems and methods described herein for schedule mimicking providevarious technical benefits and improvement to scheduling. In one aspect,the systems and methods provide for efficient utilization of computingresources by reducing the computation time required to generate aschedule. In one aspect, the methods and system reduce computations byleveraging features of schedules that are identified as favorable orgood. In embodiments, trained models are used to identify favorablefeatures as the starting point of the scheduling. The trained modelsused to identify favorable features may be faster and require lessresources than methods that rely on a global schedule search. In oneaspect, the methods use intelligent stacking of scheduling algorithms toreduce computation complexity by initially using more efficientalgorithms to mimic features of known good schedules that may be used asthe basis for further scheduling.

Some embodiments of the system, as described herein, may generate aschedule based on historic data when data about its current employees isnot available. For example, embodiments of the system may generate aschedule for a first business using historical data from a secondbusiness that is similar to the first business (and in certain cases thesimilarities may not be apparent and are determined by the system).

Some embodiments of the system may provide for bootstrap schedulingwherein the system may generate a schedule based on “profiles” and/ortemplates based on employee positions and/or roles, e.g., the generationof a schedule for an entity that does not have its own historic scheduledata using historic schedule data from other entities. Bootstrapscheduling, as described herein, may be included in module/component 126(FIG. 1 ). Embodiments of the system may provide for the selection ofone or more profile templates from a set of templates. In embodiments,the system may provide for data regarding the characteristics of a newcompany to be entered, wherein the agglomerate network matches the newbusiness to one or more profiles having one or more templates from whicha user and/or AI may select to form the basis of a new schedule for thenew business. The profiles and/or templates may be based on workerdemographics, life situations, and/or other factors, e.g., people whoare habitually late. As will be appreciated, bootstrap scheduling mayprovide a new business (without historical scheduling data) to get upand running. Embodiments of the system may be integrated with anemployee hiring platform to provide insights into whether particularroles and/or positions for a particular business are likely to result intardiness. In embodiments, the profile may be based, in part, on one ormore embeddings, as described herein. Embodiments may provide for a userto assign schedule rotations to employees.

As a non-limiting example, generating a schedule for a brand-new companyA based on historic schedule data for company B. In embodiments, B maybe company having one or more aspect/properties that are the same and/orsimilar to company A. Thus, as is to be appreciated, embodiments of thecurrent disclosure that provide for bootstrap scheduling provide for anew company to begin operations under a schedule faster than in theabsence of bootstrap scheduling, as disclosed herein. Non-limitingexamples of entity attributes which may be used for matching, asdisclosed herein, include: a number of employees, an industry, alocation, a region, a number of departments, a number of buildings, andage, etc. Non-limiting examples of position/role attributes that may beused for matching, as disclosed herein, include: shift length, requiredskill set, wage band, department, job requirements, etc.

In embodiments, schedule data, corresponding to schedules, may begenerated based on “profiles” and/or position templates, e.g., there isavailable enough data to make a profile of an employee and/or a positionwithin a company, but there is little to no historical data on thecompany itself and/or on the employee because the organization oremployee is new. For example, data for a role of type X in company B maybe used to generate a profile/template representative of the role, whichin turn may be used to generate a schedule for a role of type X incompany A, where B is a mature company with its own historic scheduledata and A is a brand new start up without any historic schedule data.

In embodiments, templates may be based on worker demographics, lifesituations, attendance rates, and/or other factors, e.g., trendsindicating people in a particular role type are habitually late,personality traits independent of a particular role, and/or the like.Embodiments may provide for a new business (e.g., one without historicalscheduling data) to get up and running sooner than in the absence ofbootstrap scheduling, as disclosed herein. Embodiments may provide forintegration with a hiring application to identify certain employeeprofiles suitable and/or preferable for hiring, e.g., engineers having aminimum number of years of experience in a particular field and/or witha particular technology. Embodiments may use a hierarchical featurepropagator (HFP) 332 (FIG. 3 ), as disclosed herein, to bootstrapconnectors and mixing of models, e.g., which modules should be usedand/or how should such modules be connected.

Embodiments may provide for a company to self-select comparable profilesfrom a set of one or more profiles. In embodiments, an artificialintelligence (AI) may match a company to a template. Embodiments mayalso provide for a hybrid approach where a company selects a profilefrom a list of profiles suggested by an AI or vice versa. Embodiments ofbootstrap scheduling may be a module that receives inputs, e.g., aschedule and/or other data, e.g., biases, as: direct input, i.e., thebootstrap scheduling module acts as a standalone module; as direct inputto an agglomerate network, i.e., without use of connectors; and/or fromconnectors, i.e., the bootstrap scheduling is one of a plurality ofmodules within an agglomerate network. For example, bootstrap schedulingmay be performed by a schedule generation module within an agglomeratenetwork that passes its output (e.g., schedules) to other modules in theagglomerate network for evaluation where the other modules generateoutput(s), e.g., a bias. The other modules may, in turn, feed the outputback into the bootstrap scheduling module to form a feedback loop whichtries to reach equilibrium and/or optimization of various biases in theagglomerate network. The connections between the bootstrap schedulingmodule and the various other modules of the agglomerate network may beaccomplished via connectors.

Accordingly, referring to FIG. 83 , an apparatus 160100 may be provided.The apparatus 160100 includes an employee data interpretation circuit160102 structured to interpret 160108 employee data 160124 correspondingto a first employee 160120; a bootstrap circuit 160104 structured to:match 160110 the first employee to a second employee 160122 via queryingone or more databases 160126 based at least in part on the employeedata; retrieve 160112 historical schedule data 160128 associated withthe second employee via querying the one or more databases; extract160114 a schedule trend 160130 from the historical schedule data;identify 160116 a portion 160132 of the historical schedule datacorresponding to the extracted schedule trend; and generate 160118,based at least in part on the identified portion, schedule data 160134corresponding to the first employee; and a schedule data provisioningcircuit 160106 structured to transmit the schedule data 160136. As willbe understood, the databases 160126 may store data for distinctentities, e.g., corporations, on the order of hundreds, thousands,tens-of-thousands, hundreds-of-thousands, millions, etc. In embodiments,the databases 160126 may be operated and/or owned by the same entitythat owns and/or operates an embodiment of an apparatus for bootstrapscheduling, as disclosed herein. In such embodiments, the entity thatowns and/or operates the databases 160126 may be the same entity thatgenerated the historic scheduling data for the entities that populatethe databases 160126. In embodiments, the databases 160126 may be ownedand/or operated by entities corresponding to the stored historicscheduling data and embodiments of the apparatus for bootstrapscheduling has network access to the databases 160126.

As will be further understood, generally, the larger the number ofdistinct corporations stored in the databases 160126, the more likely amatching corporation, position, and/or role will be found for an entity,e.g., corporation, that does not have its own historic schedule data,and/or the more closely the match will be. Automation of the matchingand schedule generation also makes it practical to search a large numberof entities for a potential match, as manual searching would likely becost-prohibitive. Thus, embodiments of the current disclosure reduce thedifficulty of getting a start-up business off the ground by helping tokeep costs associated with developing schedules for employees down.

Referring to FIG. 84 , certain further aspects of the apparatus 160100are described following, any one or more of which may be present incertain embodiments. The employee data may include at least one of: aposition, demographic information, a number of years in the position, anattendance rate, a residence, a work location, work commute data (e.g.,distance, routes, times, or traffic congestion), education level, andthe like. Matching of the first employee to the second employee may bebased at least in part on a neural network 160202 that ranks 160204 aplurality of potential matches in a query result. Use of a neuralnetwork 160202 to rank 160204 matches may provide for more accuratematching as compared to a manual matching process. The second employeematched to the first employee may be potential match result that has thehighest rank, which may be based on a number of matched properties.

Extraction of the schedule trend may be based at least in part on aneural network. The extracted trend may be an attendance rate 160206with respect to one or more of: a shift time, a number of shifts, ashift position within a workweek, a commute distance and/or time, anumber of co-workers on a shift, or a number of managers on a shift. Theportion of the historical schedule data corresponding to the extractedtrend may be less than the entire historical schedule data. The firstemployee may be further matched to a third employee 160208, wherein thefirst employee shares attributes/properties 160210 of both the secondand third employee and the schedule data is generated further based inpart on a trend extracted from historic data of the third employee. Inembodiments, the second employee and the third employee may be fromdifferent organizations. In embodiments, the second employee and thethird employee may be in different positions or roles and the firstemployee is being scheduled for a role that is a hybrid of the positionsor roles of the second and the third employee. For example, a newstartup company may have a new type of position that is a hybrid betweena traditional accounting role and a traditional engineering role, wherethe second matched employee is an accountant position from an accountingfirm and the third matched employee is an engineering role from achemical company, where the generated schedule for the hybrid positionmay incorporate aspects of the accountant position and the engineeringposition.

Matching the first employee to the second employee may be based at leastin part on employee profiles 160212. An employee profile 160212 may be acollection of worker properties, as disclosed herein, to include:demographic data, residence, level of education, job and/or positiontitle, pay grade, number of direct reports, etc. In embodiments, thefirst employee may be associated with an employer that does not havehistorical schedule data for the employee. For example, in embodiments,an existing company may hire its first full time research scientist,generate a profile for the newly hired research scientist, use theprofile, with the apparatuses and methods disclosed herein for bootstrapscheduling, to identify a matching profile for a research scientist inanother company, retrieve historic schedule data for the matchingresearch scientist, and then use the historic schedule data to generateschedule data for the newly hired research scientist.

In embodiments, the apparatus 160100 may interpret schedule scenariodata 160214 to determine a type of data for inclusion in the generationof the schedule data. In embodiments, the schedule data corresponding tothe first employee may include shift data 160216 that indicates thefirst employee is available to work a shift corresponding to the secondemployee.

Referring to FIG. 85 , a method 160300 for bootstrap scheduling isshown. The method may be performed via apparatus 160100 and/or any othercomputing device disclosed herein. In embodiments, the method 160300includes interpreting, via an employee data interpretation circuit,employee data corresponding to a first employee 160302; matching, via abootstrap circuit, the first employee to a second employee via queryingone or more databases based at least in part on the employee data160304. The method 160300 further includes retrieving, via the bootstrapcircuit, historical schedule data associated with the second employeevia querying the one or more databases 160306; and extracting, via thebootstrap circuit, a schedule trend from the historical schedule data160308. The method 160300 further includes identifying, via thebootstrap circuit, a portion of the historical schedule datacorresponding to the extracted schedule trend 160310; and generating,via the bootstrap circuit and based at least in part on the identifiedportion, schedule data corresponding to the first employee 160312. Themethod 160300 further includes transmitting, via a schedule dataprovisioning circuit, the schedule data 160314.

Referring to FIG. 86 , certain further aspects of the method 160300 aredescribed following, any one or more of which may be present in certainembodiments. For example, in embodiments, the employee data may includeat least one of: a position, demographic information, a number of yearsin the position, an attendance rate, a residence, a work location, workcommute data (e.g., distance, routes, or traffic congestion), educationlevel, and the like. Matching of the first employee to the secondemployee may be based at least in part on a neural network 160402 thatranks 160404 a plurality of potential matches in a query result. Inembodiments, the neural network may be trained over a training datasetof labeled query results each having a score corresponding to an initialemployee profile (to which the results are matched). Extraction of theschedule trend may be based at least in part on a neural network. Theextracted trend 160420 may be an attendance rate 160406 with respect toone or more of: a shift time, a number of shifts, a shift positionwithin a workweek, a commute distance, a number of co-workers on ashift, or a number of managers on a shift. The portion of the historicalschedule data may be less than the entire historical schedule data. Thefirst employee may be further matched to a third employee 160408,wherein the first employee shares attributes or properties 160410 ofboth the second and third employee and the schedule data may begenerated further based in part on a trend extracted from historic dataof the third employee. The second employee and the third employee may befrom different organizations. The second employee and the third employeemay be in different positions or roles and the first employee may bescheduled for a role that is a hybrid of the positions or roles of thesecond and the third employee. Matching the first employee to the secondemployee may be based at least in part on employee profiles 160412. Thefirst employee may be associated with an employer that does not havehistorical schedule data for the employee. In embodiments, the method160300 may further include interpreting schedule scenario data 160414 todetermine a type of data for inclusion in the generation of the scheduledata. The schedule data corresponding to the first employee may includeshift data 160416 that indicates the first employee is available to worka shift corresponding to the second employee.

Referring to FIG. 87 , an apparatus 160500 for bootstrap scheduling, inaccordance with embodiments of the current disclosure, is shown. Theapparatus 160500 includes a position data interpretation circuit 160502structured to interpret 160508 position data 160524 corresponding to afirst position 160520. The apparatus 160500 further includes a bootstrapcircuit 160504 structured to: match 160510 the first position to asecond position 160522 via querying one or more databases 160526 basedat least in part on the position data; retrieve 160512 historicalschedule data 160528 associated with the second position via queryingthe one or more databases; extract 160514 a schedule trend 160530 fromthe historical schedule data; identify 160516 a portion 160532 of thehistorical schedule data corresponding to the extracted schedule trend;and generate 160518, based at least in part on the identified portion,schedule data 160534 corresponding to the first position. The apparatus160500 further includes a schedule data provisioning circuit 160506structured to transmit 160536 the schedule data.

Referring to FIG. 88 , certain further aspects of the apparatus 160500are described following, any one or more of which may be present incertain embodiments. In embodiments, the position data may include atleast one of: average hours worked; average days worked; average numberof managers on co-shift; average number of coworkers on co-shift;average pay; average start time; average stop time; average number ofoff-hours (downtime); legal requirements; etc. The first position may beassociated with an employer 160602 that does not have historicalschedule data for the first position.

Referring to FIG. 89 , a method 160700 for bootstrap scheduling, inaccordance with embodiments of the current disclosure, is shown. Themethod 160700 may be performed via apparatus 160500 and/or any othercomputing device disclosed herein. The method 160700 includesinterpreting, via a position data interpretation circuit, position datacorresponding to a first position 160702; matching, via a bootstrapcircuit, the first position to a second position via querying one ormore databases based at least in part on the position data 160704; andretrieving, via the bootstrap circuit, historical schedule dataassociated with the second position via querying the one or moredatabases 160706. The method 160700 further includes extracting, via thebootstrap circuit, a schedule trend from the historical schedule data160708; identifying, via the bootstrap circuit, a portion of thehistorical schedule data corresponding to the extracted schedule trend160710; and generating, via the bootstrap circuit and based at least inpart on the identified portion, schedule data corresponding to the firstposition 160712. The method 160700 further includes transmitting, via aschedule data provisioning circuit, the schedule data 160714.

Referring to FIG. 90 , certain further aspects of the method 160700 aredescribed following, any one or more of which may be present in certainembodiments. In embodiments, the position data may include at least oneof: average hours worked; average days worked; average number ofmanagers on co-shift; average number of coworkers on co-shift; averagepay; average start time; average stop time; average number of off-hours(downtime); legal requirements; and the like. In embodiments, the firstposition 160820 may be associated with an employer 160802 that does nothave historical schedule data for the first position.

Referring to FIG. 91 , a non-transitory computer-readable medium 160900,in accordance with embodiments of the current disclosure, is shown. Thenon-transitory computer-readable medium 160900 includes instructionsthat adapt at least one processor to: interpret employee datacorresponding to a first employee 160902; match the first employee to asecond employee via querying one or more databases based at least inpart on the employee data 160904; and retrieve historical schedule dataassociated with the second employee via querying the one or moredatabases 160906. The instructions further adapt the at least oneprocessor to extract 160908 a schedule trend from the historicalschedule data; identify a portion of the historical schedule datacorresponding to the extracted schedule trend 160910; and generate,based at least in part on the identified portion, schedule datacorresponding to the first employee 160912. In embodiments, theinstructions further adapt the at least one processor to transmit theschedule data 160914.

Referring to FIG. 92 , certain further aspects of the non-transitorycomputer-readable medium 160900 are described following, any one or moreof which may be present in certain embodiments. In embodiments, thefirst employee may be further matched to a third employee 161008,wherein the first employee shares attributes or properties 161010 ofboth the second and third employee and the schedule data is generatedfurther based in part on a trend extracted from historic data of thethird employee. In embodiments, the second employee and the thirdemployee may be from different organizations. In embodiments, the secondemployee and the third employee may be in different positions or rolesand the first employee may be scheduled for a role that is a hybrid ofthe positions or roles of the second and the third employee.

Referring to FIG. 93 , a non-transitory computer-readable medium 161100may be provided. The non-transitory computer-readable medium 161100includes instructions that adapt at least one processor to: interpretposition data corresponding to a first position 161102; match the firstposition to a second position via querying one or more databases basedat least in part on the position data 161104; and retrieve historicalschedule data associated with the second position via querying the oneor more databases 161106. The instructions further adapt the at leastone processor to extract a schedule trend from the historical scheduledata 161108; identify a portion of the historical schedule datacorresponding to the extracted schedule trend 161110; and generate,based at least in part on the identified portion, schedule datacorresponding to the first position 161112. The instructions furtheradapt the at least one processor to transmit the schedule data 161114.

Referring to FIG. 94 , certain further aspects of the non-transitorycomputer-readable medium 161100 are described following, any one or moreof which may be present in certain embodiments. In embodiments, theposition data includes at least one of: average hours worked; averagedays worked; average number of managers on co-shift; average number ofcoworkers on co-shift; average pay; average start time; average stoptime; average number of off-hours (downtime); legal requirements; andthe like. In embodiments, the first position 161220 may be associatedwith an employer 161202 that does not have historical schedule data forthe first position.

Referring to FIG. 95 , an apparatus 161300 for bootstrap scheduling, inaccordance with embodiments of the current disclosure, is shown. Theapparatus 161300 includes an employee data interpretation circuit 161302structured to interpret 161308 first employee profile data 161318corresponding to a first employee 161316; a bootstrap circuit 161304structured to: match 161310 the first employee profile data to a secondemployee profile data 161320 via querying one or more databases 161322;and retrieve 161312 historical schedule data 161324 associated with thesecond employee profile data via querying the one or more databases. Thebootstrap circuit 161304 is further structured to generate 161314, basedat least in part on the retrieved historical schedule data, scheduledata 161326 corresponding to the first employee. The apparatus 161300further includes a schedule data provisioning circuit 161306 structuredto transmit 161328 the schedule data.

Referring to FIG. 96 , certain further aspects of the apparatus 161300are described following, any one or more of which may be present incertain embodiments. In embodiments, the bootstrap circuit is furtherstructured to: interpret a plurality of employee profile data returnedvia querying the one or more databases 161402. In such embodiments, foreach of the plurality of returned employee profile data, the bootstrapcircuit 161304 may: weight the returned employee profile data based on acloseness of the match between the returned employee profile data andthe first employee profile data 161404; and select, based at least inpart on the weightings, the second employee profile data from theplurality of returned employee profile data 161406. In embodiments, thecloseness of the match may be based at least in part on an employeeprofile property 161410 common between the first employee profile dataand the plurality of returned employee profile data. The employeeprofile property may include: an on-time rating, a tardiness rating, afriendliness rating, an efficacy rating, and the like. In embodiments,the second employee profile data may correspond to an average of aplurality of employee profiles.

Referring to FIG. 97 , a method 161500 for bootstrap scheduling, inaccordance with embodiments of the current disclosure, is shown. Themethod 161500 includes interpreting, via an employee data interpretationcircuit, first employee profile data corresponding to a first employee161502; matching, via a bootstrap circuit, the first employee profiledata to second employee profile data via querying one or more databases161504; and retrieving, via the bootstrap circuit, historical scheduledata associated with the second employee profile data via querying theone or more databases 161506. In embodiments, the method 161500 mayfurther include generating, via the bootstrap circuit, based at least inpart on the retrieved historical schedule data, schedule datacorresponding to the first employee 161508; and transmitting, via aschedule data provisioning circuit, the schedule data 161510.

Referring to FIG. 98 , certain further aspects of the method 161500 aredescribed following, any one or more of which may be present in certainembodiments. In embodiments, matching the first employee profile data tothe second employee profile data further includes: interpreting aplurality of employee profile data returned via querying the one or moredatabases 161602. The method 161500 may further include: for each of theplurality of returned employee profile data, weighting the returnedemployee profile data based on a closeness of the match between thereturned employee profile data and the first employee profile data161604; and selecting, based at least in part on the weightings, thesecond employee profile data from the plurality of returned employeeprofile data 161606. As disclosed herein, in embodiments, the closenessof the matching may be based at least in part on an employee profileproperty 161610 common between the first employee profile data and theplurality of returned employee profile data. In embodiments, theemployee profile property includes one or more of: an on-time rating, atardiness rating, a friendliness rating, an efficacy rating, and thelike. The second employee profile data may correspond to an average of aplurality of employee profiles.

Referring to FIG. 99 , a non-transitory computer-readable medium 161700may be provided. The non-transitory computer-readable medium 161700includes instructions that adapt at least one processor to: interpretfirst employee profile data corresponding to a first employee 161702;match the first employee profile data to second employee profile datavia querying one or more databases 161704; and retrieve historicalschedule data associated with the second employee profile data viaquerying the one or more databases 161706. The instructions furtheradapt the at least one processor to: generate based at least in part onthe retrieved historical schedule data, schedule data corresponding tothe first employee 161708; and transmit the schedule data 161710.

Referring to FIG. 100 , certain further aspects of the non-transitorycomputer-readable medium 161700 are described following, any one or moreof which may be present in certain embodiments. In embodiments, matchingthe first employee profile data to the second employee profile dataincludes: interpreting a plurality of employee profile data returned viaquerying the one or more databases 161802; and, for each of theplurality of returned employee profile data, weighting the returnedemployee profile data based on a closeness of the match between thereturned employee profile data and the first employee profile data161804. In embodiments, matching the first employee profile data to thesecond employee profile data further includes selecting, based at leastin part on the weightings, the second employee profile data from theplurality of returned employee profile data 161806. As disclosed herein,in embodiments, the closeness of the matching is based at least in parton an employee profile property 161810 common between the first employeeprofile data and the plurality of returned employee profile data. Theemployee profile property may include one or more of: an on-time rating,a tardiness rating, a friendliness rating, an efficacy rating, and thelike. In embodiments, the second employee profile data corresponds to anaverage of a plurality of employee profiles.

Illustrated in FIG. 101 is another method 161900 for bootstrapscheduling, in accordance with embodiments of the current disclosure.The method 161900 may be performed via the apparatus 161300 and/or anyother computing device disclosed herein. The method 161900 includesinterpreting employee data corresponding to an employee 161902; matchingthe employee to an employee profile based at least in part on theemployee data 161904; retrieving historical schedule data associatedwith the employee profile 161906; generate, based at least in part onthe historical schedule data, schedule data corresponding to theemployee 161908; and transmitting the schedule data 161910. Inembodiments, the employee profile may correspond to and/or otherwise berepresentative of a single employee. In embodiments, the employeeprofile may correspond to and/or otherwise be representative of aplurality of employees, e.g., the employee profile may be a compositeaverage generated by data corresponding to more than one employee.

One non-limiting use case of the method 161900 includes a scenario wherea newly formed business hires an initial set of employees and, as such,does not have any historical data for the initial set of employees. Theinitial set of employees may include different types of employees, e.g.,engineers, accountants, human resource specialists, etc. Each of theinitial set of employees may also have different work ethics, e.g., hardworkers, procrastinators, habitually tardy, etc. A plurality of employeeprofiles each corresponding to one of a combination of employee typesand work ethics may have been generated based on historic data collectedfrom a large number of employee in the workforce, e.g., hundreds ofemployees, thousands of employees, tens-of-thousands of employees,hundreds-of-thousands of employees, etc. Each of the employee profilesmay have a corresponding schedule and/or schedulingconstraints/attributes based on the historic data collected from thelarge number of employees. Non-limiting examples of suchconstraints/attributes include: needs a shift with explicit start andstop times; likely to not return (or return late) if allowed to gooffsite for breaks; likely to return if allowed to go offsite forbreaks; can work a shift unsupervised; cannot work a shift unsupervised,etc. It will be understood that the employee profiles may not have a1-to-1 correspondence with the initial set of employees and, as such,each of the initial set of employees may be matched to one or more ofthe employee profiles. For example, one of the initial set of employeesmay be a hybrid between an engineer, accountant, and front-end salesmanager. As such, the hybrid employee may be matched to three differentemployee profiles. In embodiments where an employee is matched to morethan one profile, the one or more profiles may be weighted, for purposesof schedule data generation, based on a weighting of the different rolesfor the employee. For example, the hybrid employee may spend 60% oftheir time in the engineering role, 10% of their time in the accountantrole, and 30% of their time in the front-end sales manger role. As such,a first matched profiled for the hybrid employee may corresponds to theengineering role and receive a weighting of 60%; a second matchedprofiled for the hybrid employee may corresponds to the accountant roleand receive a weighting of 10%; and a third matched profiled for thehybrid employee may corresponds to the front-end sales manager role andreceive a weighting of 30%.

The systems and methods described herein for bootstrap schedulingprovide various technical benefits and improvements over known methods.In one aspect, the systems and methods improve aspects of training andutilization of models. Embodiments of the systems and methods describedherein improve the adaptability of trained models to different datasets. Embodiments of the systems and methods described herein improvethe adaptability of trained models to scenarios and data for which themodels were not explicitly trained. In one example, models are adaptedto new data with company templates. Company templates are used as dataapproximations allowing the use of models where data is not yetavailable or there is not enough data to execute a model.

Embodiments may include agglomerate model feature space connectors.Embodiments of the system may include Agglomerate Model Feature SpaceConnectors that provide for an intelligent connection between the outputof one agglomerated model and the input of a second agglomerated model.The connectors support the communication of value, confidence andoptimization surface descriptors between agglomerate models.

Embodiments may include serially connected agglomerate models. Inembodiments, a first agglomerated model may be serially connected to asecond agglomerated model such that an output of the first agglomeratedmodel becomes an input/feature of a second agglomerated model. A nextagglomerated model may then be connected to the first or secondagglomerated model, or alternatively, the first and second agglomeratedmodel, such that an output(s) of the first agglomerate model and/or anoutput(s) of the second agglomerated model are inputs of the nextagglomerated model. Such a connection is referred to herein as anAgglomerate Model Feature Space Connector. If a set of connectedagglomerated models are connected in such a manner that the connectionsdo not form a loop, the set of agglomerated models are seriallyconnected. A serially connected set of agglomerated models may includeone or more agglomerated models.

Embodiments may include recursively connected agglomerate models.Alternatively, in embodiments, a first set of serially connectedagglomerated models may be recursively connected to a second set ofserially connected agglomerated models such that an output or outputs ofthe first set of agglomerated models is an input or inputs of a secondset of serially connected agglomerated, and for which an output oroutputs of the second set of serially connected agglomerated is an inputor inputs for the first set of serially connected agglomerated models.

Embodiments may include feature value descriptor. In embodiments, anygiven agglomerate model may output a feature value that is consumed byanother agglomerate model, directly, after the application of a featurespace conversion, or in combination with other feature values. Featurevalues may be represented by any type of number, string, object, list,array, map, feature value description, feature value or combinationthereof.

In embodiments, the feature value descriptor contains a feature valueand/or other information describing model confidence, e.g., likelihoodthat the actual behavior results in the feature value output by theagglomerate model; likelihood that the actual behavior returns resultsbetter than the modeled feature value output; likelihood that the actualbehavior returns results worse than the feature value; a list of outputsand likelihoods; a feature value surface describing the feature valueand how the value responds to changes to a feature value input; or anycombination thereof.

In an embodiment, a feature value descriptor contains one or more of afeature value for feature values that may be represented by a continuousfunction in the region of the feature value, a description of how thefeature value responds to changes in one or more agglomerate modelfeature input values, where the feature input values define theconditions over which a given agglomerate model is executed, or forwhich the output of the agglomerate model includes an output featurevalue which the Feature Value Descriptor corresponds to.

In embodiments, a feature value descriptor may represent anon-continuous set of results. Alternatively, the feature valuedescriptor may contain a feature value that is represented by anon-continuous function.

In embodiments, a feature value descriptor may represent a set ofvalues, which may vary along some axes non-continuously, and which maybe represented along other axes by continuous functions.

In embodiments, a feature value descriptor (or descriptors) may describethe modeled or learned behavior of the feature value descriptor inresponse to changes in another feature value descriptor. As will beunderstood, in many cases, the other feature value descriptor mightrepresent an input to the agglomerate model generating the output value,but more generically, the feature value descriptor might describe how afeature value might respond to changes in any correlated feature value.

Embodiments may include feature value descriptor generation. In someembodiments, an agglomerate model might generate feature valuedescriptors directly. In other embodiments, an agglomerate model mightgenerate partially complete feature value descriptors, and thehierarchical feature propagator determines if feature value descriptorinformation is desired, and if so, it may add additional valuedescription information. The hierarchical feature propagator and othersystem components may operate in one of three modes: Learning Mode;Mapping Mode; and Extrapolation Mode.

In learning mode, the system may run one or more agglomerate modelsusing retrospective data, and variations of input data where uncertaintyexists, or where variations of input data are desirable to develop abetter understanding of the modeled response about the expected output.In learning mode, the retrospective data may be used to reinforce(positively or negatively) to improve the performance of an agglomeratemodel. In addition to, or as an alternative, the retrospective data maybe used to model (learn) perceived biases in the agglomerate modeloutput, to model (learn) the actual sensitivity of an agglomerate modelfeature value output to changes to the agglomerate model inputs. Inlearning mode, the system may run zero, one or more “test” runs togenerate outputs that match the actual, retrospective values achieved,and to use these runs to reinforce agglomerate model behaviors or tolearn how the Hierarchical Feature Propagator may better developeffective Feature Value Descriptors based on missing or incompletedescriptors. In Learning Mode, the system might train over a relativelylarge number of “test points”. This mode may be preferentially designedto run when processing resources are available, are preferentiallypriced, when deviations in the predicted values & confidences from anagglomerate model fall outside of expectations, or when there is adesire to improve the training of one or more agglomerate models, orwhen there is a desire to improve the training of the HierarchicalFeature Propagator.

In Mapping Mode, the system may run one or more agglomerate models insuch a way that multiple output feature values are produced, such thatinstead of producing a feature value output incorporating a singlepredicted value, a feature value descriptor can better describe how afeature value might respond to changes in any correlated feature value.As the hierarchical feature propagator “learns” about the variation of agiven feature value output based on changes to another correlatedfeature value or feature value input, the hierarchical featurepropagator will learn to propagate sufficiently reliable results withfewer agglomerate model runs.

In extrapolation mode, the hierarchical feature propagator fills inmissing feature value data and/or descriptions to generate feature valuedescriptors.

Embodiments may include hierarchical feature propagation. As will beunderstood, embodiments of the disclosure may provide for aself-organizing agglomerate scheduler, which may form part ofmodule/component, e.g., 124 (FIG. 1 ), and/or the HFP 332 (FIG. 2 ). Forexample, to prevent overfitting and to account for sparse or missingdata, the Hierarchical Feature Propagator may propagate feature valueresults which blend results from historical runs, other locations and/orgroups, correlated industry values, etc. In embodiments, thehierarchical feature propagator may fill-in missing data based on othercomparable runs.

Referring now to FIG. 102 , embodiments of an agglomerate network 30100may include one or more self-organizing circuits 30110 which, asdescribed in herein, may be structured to determine which modules 30112,30114, 30116, 30118, and/or connectors 30120, 30122 should be includedin the agglomerate network 30100 and/or how toarrange/assembly/configure them. As disclosed herein, an agglomeratenetwork, e.g., 30110, may include two or more modules 30112, 30114,30116, 30118 that manipulate a common set of scheduling data 30126. Thenumber of modules, e.g., 30112, 30114, 30116, 30118, within anagglomerate network may range from: 2-1,000; 2-500; 2-100; 2-50; 2-25;2-10; 2-5. Module/circuit types may include schedulers, weatherforecasters, retention predictors, sales predictors, connectors, and/orany type of agglomerate network circuit and/or connector describedherein. Embodiments of the organizing circuit 30110 may use artificialintelligence to select which agglomerate network circuits and/orconnectors are included in the agglomerate network 30100. Embodiments ofthe organizing circuit 30110 may determine available data sets, e.g.,current weather information, current traffic patterns, etc., and thenselect agglomerate network circuits and/or connectors for inclusion inan agglomerate network based on available data sets 30128.

Certain embodiments may configure the network 30100 according toconfidence metrics 30130 for the available data 30128, modules 30112,30114, 30116, 30118, and the like. In some embodiments, the agglomeratenetwork 30100 may reorganize in real-time. Agglomerate networkmodules/circuits, e.g., 30112, 30114, 30116, 30118, and connectors,e.g., 30120, 30122, may be added based on confidence metrics 30130 ofmodules for industry, location, and the like. For example, a module thathas a low confidence of its predicted output may be dynamically removedfrom the network 30100. A confidence metric 30130 may be generated byits corresponding agglomerate network module, e.g., 30112, 30114, 30116,30118 and/or connector 30120, 30122. In embodiments, an agglomeratenetwork module, e.g., 30112, 30114, 30116, 30118 and/or connector 30120,30122, may be removed if its corresponding confidence level exceeds athreshold, e.g., a lower bound, which may be predetermined ordynamically determined. For example, a weather sales predictionmodule/circuit may be excluded from an agglomerate network if aconfidence metric for predicting sales based on the weather is less thana desirable level, e.g., 80%.

In embodiments, an agglomerate network may include one or moreself-organizing modules 30110 that parse and/or interpret parameters fora particular scheduling scenario for processing either by logic gatesand/or an AI module to generate a list of modules, e.g., 30112, 30114,30116, 30118, for inclusion in the agglomerate network. For example, ifthe available data sets 30128 include weather data, then the list ofmodules may include two or more weather modules which are available forinclusion in the agglomerate network. The list may be pruned based onthe confidence metrics 30130, e.g., only weather modules with highconfidence levels, e.g., >80%, may be placed on the list. Inembodiments, the logic gates and/or the AI module may also selectconnectors, e.g., 30120, 30122, and/or an arrangement for theconnectors, i.e., how the connectors connect the various includedmodules in the agglomerate network.

Embodiments may also provide for a training process of the AI module(s),e.g., how the AI module(s) learn to select agglomerate networkmodules/circuits, connectors, and/or arrangement of connectors. Suchtraining may include a training set that includes various types of mockavailable data sets, e.g., 30130, paired/labeled with variousconfigurations of agglomerate network circuits/modules connected viaconnectors, where the AI learns to generate agglomerate networkconfigurations based on the training set.

Shown in FIG. 103 is an apparatus 30200 for self-organizing anagglomerate network, e.g., 30100 (FIG. 102 ). The apparatus 30200 may beincluded in the agglomerate network 30100 as an organizing circuit 30110(FIG. 1 ) or it may be apart, e.g., “outside”, of any agglomeratenetwork. In embodiments, the apparatus 30200 may be a dedicated hardwaredevice or form part of any computing device disclosed herein.Embodiments of the apparatus 30200 may include a scenario interpretationcircuit 30210, a scenario analysis circuit 30212, an architect circuit30214, and/or an architecture provisioning circuit 30215. The scenariointerpretation circuit 30210 may be structured to interpret schedulescenario data 30216. The scenario analysis circuit 30212 may bestructured to extract one or more scenario elements 30218 from theschedule scenario data 30216. The architect circuit 30214 may bestructured to: identify, based at least in part on the one or morescenario elements 30218, one or more agglomerate modules/circuits, e.g.,30112, 30114, 30116, 30118, and one or more connector circuits, e.g.,30120, 30122; and generate agglomerate network architecture data 30220that defines, in part, a structural relationship 30222 between at leastone of the one or more agglomerate network circuits and at least one ofthe one or more connector circuits. The architecture provisioningcircuit 30215 may be structured to transmit the agglomerate networkarchitecture data 30220.

In embodiments, the one or more scenario elements 30218 may include atleast one of: a weather event, a start date of a scenario and/or an enddate of the scenario, a profile of an entity, a maintenance schedule,shipment schedule, a delivery schedule, etc. In embodiments,identification of the one or more agglomerate network modules/circuits,e.g., 30112, 30114, 30116, 30118, and the one or more connectorcircuits, e.g., 30120, 30122, may be based at least in part on acombination of scenario elements 30218. In embodiments, the profile ofthe entity may include at least one of: a listing of employees, alisting of positions, one or more site locations, a budget, etc.

In embodiments, identifying the one or more agglomerate networkmodules/circuits, e.g., 30112, 30114, 30116, 30118, and the one or moreconnector circuits, e.g., 30120, 30122, may be based at least in part onone or more confidence metrics 30130 (FIG. 102 ), e.g., ratings whichmay be generated via artificial intelligence. For example, an artificialintelligence may be trained to recognize which agglomerate networkcircuit(s) best correspond to a given class of scenario element, e.g.,weather modules/circuits for time periods falling over the winter in anorthern geographic location. In embodiments, the one or moreagglomerate modules/circuits and the one or more connectormodules/circuits may be selected from a predetermined listing of thesame, wherein the listing is either directly provided (e.g., hard codedor entered via a user) or retrieved from a database.

Illustrated in FIG. 104 is a method 30300 for self-organizing anagglomerate network e.g., 30100 (FIG. 102 ). The method 30300 may beperformed via apparatus 30200 (FIG. 103 ) and/or any other computingdevice disclosed herein. The method 30300 may include interpreting, viaa scenario interpretation circuit, schedule scenario data 30310. Themethod 30300 may further include extracting, via a scenario analysiscircuit, one or more scenario elements from the schedule scenario data30312. The method 30300 may further include identifying, via anarchitect circuit and based at least in part on the one or more scenarioelements, one or more agglomerate network circuits and one or moreconnector circuits 30314. The method 30300 may further includegenerating, via the architect circuit, agglomerate network architecturedata that defines, in part, a structural relationship between at leastone of the one or more agglomerate network circuits and at least one ofthe one or more connector circuits 30316. The method 30300 may furtherinclude transmitting, via an architecture provisioning circuit, theagglomerate network architecture data 30318. In embodiments, the method30300 may further include generating a schedule via the one or moreagglomerate network circuits 30320. In embodiments, the method mayfurther include updating and/or revising the agglomerate networkarchitecture data 30322, which may be in in real-time or near-real time.The update(s) and/or revision(s) may be based on updates to thescheduling scenario. The update(s) and/or revision(s) may be based onfeedback/results from execution of the agglomerate network in generatinga schedule for the scenario.

Referring now to FIG. 105 , another apparatus 30400 for self-organizingan agglomerate network e.g., 30100 (FIG. 102 ) is shown. The apparatus30400 may be included in the agglomerate network 30100 as an organizingcircuit 30110 (FIG. 102 ) or it may be apart, e.g., “outside”, of anyagglomerate network. In embodiments, the apparatus 30400 may be adedicated hardware device or form part of any computing device disclosedherein. The apparatus 30400 may include a scenario interpretationcircuit 30410, a scenario analysis circuit 30412, an architect circuit30414, and/or an assembly circuit 30416. The scenario interpretationcircuit 30410 may be structured to interpret schedule scenario data30418. The scenario analysis circuit 30412 may be structured to extractone or more scenario elements 30420 from the schedule scenario data30418. The architect circuit 30414 may be structured to identify, basedat least in part on the one or more scenario elements 30420, one or moreagglomerate modules/circuits, e.g., 30112, 30114, 30116, 30118 and oneor more connector modules/circuits, e.g., 30120, 30122. The architectcircuit 30414 may be further structured to generate agglomerate networkarchitecture data 30422 that defines, in part, one or more structuralrelationships 30424 between at least one of the one or more agglomeratemodules/circuits and at least one of the one or more connectormodules/circuits. The assembly circuit 30416 may be structured toassemble the one or more agglomerate modules/circuits, e.g., 30112,30114, 30116, 30118, and/or the one or more connector modules/circuits,e.g., 30120, 30122 based at least in part on the one or more structuralrelationships 30424. One non-limiting example of assembling the one ormore agglomerate modules/circuits, e.g., 30112, 30114, 30116, 30118,and/or the one or more connector modules/circuits, e.g., 30120, 30122includes adjusting a connector module/circuit such that theconnector/module circuit has access to a memory location storing theoutput(s) of an agglomerate module/circuit, which the connectormodule/circuit may treat as an input, and/or adjusting a connectormodule/circuit such that the connector module/circuit has access to amemory location accessed by an agglomerate network circuit for receivinginput(s). Another non-limiting example of assembling the one or moreagglomerate modules/circuits, e.g., 30112, 30114, 30116, 30118, and/orthe one or more connector modules/circuits, e.g., 30120, 30122 includesusing an intermediate data array to store outputs and/or inputs ofagglomerate network circuits, wherein the connectors store inputs and/oroutputs of the agglomerate network circuits so that the inputs and/oroutputs are accessible via other connectors which may pass the inputsand/or outputs in the array to one or more agglomerate network circuits.In other words, embodiments of the current disclosure may use a dataarray, or other data structure, to store data in a manner commonlyaccessible by the connectors and/or agglomerate network circuits withinan agglomerate network.

Accordingly, connector circuits/modules may facilitate a 1-to-1connection between agglomerate network circuits/models; connectorcircuits/modules may facilitate a 1-to-many connection betweenagglomerate network circuits/models; connector circuits/modules mayfacilitate a many-to-1 connection between agglomerate networkcircuits/models; and/or connector circuits/modules may facilitate amany-to-many connection between agglomerate network circuits/models. Inembodiments, connector circuits/modules receive and/or access dataoutputted by an agglomerate network circuit and pass the data, or dataderived from the output data, as input to the same agglomerate networkcircuit.

Illustrated in FIG. 106 is another method 30500 for self-organizing anagglomerate network e.g., 30100 (FIG. 102 ). The method 30500 may beperformed via apparatus 30400 (FIG. 105 ) and/or any other computingdevice disclosed herein. The method 30500 may include interpreting, viaa scenario interpretation circuit, schedule scenario data 30510. Themethod 30500 may include extracting, via a scenario analysis circuit,one or more scenario elements from the schedule scenario data 30512. Inembodiments, the extracted one or more scenario elements may include aweather event, a sales event, a holiday event, a location, and/or aperiod of time. The method 30500 may include identifying, via anarchitect circuit and based at least in part on the one or more scenarioelements, one or more agglomerate network circuits and one or moreconnector circuits 30514. The method 30500 may include generating, viathe architect circuit, agglomerate network architecture data thatdefines, in part, one or more structural relationships between at leastone of the one or more agglomerate network circuits and at least one ofthe one or more connector circuits 30516. The method 30500 may furtherinclude assembling, via an assembly circuit, the one or more agglomeratenetwork circuits and the one or more connector circuits based at leastin part on the one or more structural relationships 30518. Inembodiments, the method 30500 may include generating a schedule via theone or more agglomerate network circuits 30520. In embodiments, themethod 30500 may further include updating and/or revising theagglomerate network architecture data in real-time and/or near-real time30522, as described herein.

As will be appreciated, self-organizing agglomerate networks reduce theamount of time for configuring/tailoring agglomerate networkcircuits/modules and/or connectors for a particular scheduling scenario,as compared to manually assembling an agglomerate network. Further,embodiments of self-organizing agglomerate networks may be able toachieve tighter tailoring, e.g., they include more relevant agglomeratecircuits/modules and/or data flow structures, than manual approaches asthe artificial intelligence is able to process and analyze schedulescenario data with a degree of detail and speed not practicallyachievable by a human mind. For example, embodiments of theself-organizing network may be able to assemble themselves withinseconds, minutes, and/or hours after the scheduling scenario data hasbecome available; and such self-organizing networks may have tens,hundreds, or thousands of agglomerate network circuits and/orconnectors. As will be appreciated, the ability to produce highlytailored agglomerate networks in such periods of time improves theability of an entity, for which the agglomerate network is being used togenerate schedule for, to adapt to changing environments, e.g., weatherpatterns, seasonal holiday traffic, sporting events, and/or other typesof scenario, as described herein, which can affect the efficiency of aschedule, e.g., was the schedule over or understaffed, too strenuous onthe employees, or too lenient to the employees, etc.

Embodiments may include interaction with autonomous evolutioncontroller. In embodiments, the Hierarchical Feature Propagator operatesin conjunction with the Autonomous Evolution Propagator to determine howmany evolutions of an agglomerate model should be executed to generateappropriately detailed feature value descriptors. For example, incertain situations as represented by available agglomerate model inputs,agglomerate model output confidences, and Hierarchical FeaturePropagator Extrapolation Mode performance, the system may determine thatexecuting multiple agglomerate model runs is not necessary as the inputand output feature values meet confidence thresholds, and thus, theHierarchical Feature Propagator may operate in a more efficientextrapolation mode. In other cases, however, the Autonomous EvolutionController may determine several alternative runs of an agglomeratemodel to better understand the sensitivity of the output to changes ininput (known or unknown) conditions. In cases where the feature outputsrepresent a function that does not continuously vary over at least oneinput value, the Autonomous Evolution Propagator may determine if asingle “most likely” output value is sufficient or whether a set of themost likely output values is desirable. In cases where multipleagglomerated models are connected recursively, the Autonomous EvolutionController determines how many iterations of a recursive loop arerequired, or when a recursive loop has successfully returned featurevalue outputs that meet acceptable confidence criteria. In embodiments,the recursive loop, or number of iterations, may stop when a set ofagglomerate models have considered one or more of: at least one input ofdata, at least an input of another models' output, an input containing aportion of the set of agglomerate models' own output, e.g., onerecursion, a particular number of votes, e.g., for a schedule orfeature, a particular confidence threshold, and/or an estimate ofpropagation in a graph or tree of an input. As a non-limiting example ofan estimate of propagation on an input, for a balanced binary treearrangement of n models in an agglomerate network, there may be log(n)steps in time to perform one propagation since the height of the tree islog(n) and each level of the tree can be processed in one step. If theobjective were to have every node consider the output of every othernode at least once, there would have to be two propagations through therecursive binary tree. Thus, for example, the root model in steplog(n)+1 would be able to consider the recursed output of the leafmodels that ran in step log(n). Similarly, in embodiments, the leafmodels of the tree may only consider the output of other leaf models inthe last step which would be step 2 log(n).

In embodiments, Hierarchical Feature Propagator connectors may take inadditional variables to help address inputs not accounted for in aprimary trained model, or it can use a connector to merge the resultsfrom multiple different models/model runs, or it may use the connectorto mix aggregate model results with the primary model results to accountfor sparse data, or recognizing that results from aggregate models mayimprove results and reduce over fitting susceptibility.

Embodiments may include aggregating agglomerate scheduling models overvariable time horizons. Embodiments of the agglomerate network, e.g.,multiple neural networks interfaced together as described herein, may beused by the system to provide for extended horizon scheduling. Extendedhorizon scheduling may be performed as part of modules/components 124,126, 128, 130, and/or 132 (FIG. 1 ). For example, embodiments of thesystem, as described herein, may use an agglomerate network to generatean employee work schedule, wherein the agglomerate network takes intoaccount long term company objectives, e.g., employee retention. Forexample, embodiments of the system, as described herein, may mergeresults from scheduling programs operating at different levels ofspecificity, e.g., scheduling individuals versus groups of individuals,and across multiple time horizons. For example, an embodiment of thesystem includes an agglomerated project scheduler that develops aproject schedule based on the tasks that are required to be performedover a given time period. This model may run across several projects,each with its own requirements, own time horizons, etc.

In embodiments, extended horizon scheduling may be scheduling that takesinto account and/or otherwise seeks to improve and/or maintain one ormore long-term company/entity objectives, e.g., corporate objectivesthat generally takes two or more months to achieve. It is to beunderstood, however, that embodiments of the current disclosure may seekto improve and/or maintain the objectives disclosed herein on ashort-term basis, e.g., per day, week, and/or a month. Non-limitingexamples of such objectives include: employee retention, morale,profitability, cash flow, company survival, stock value/price, a numberof sales, an amount of profit, a cash flow, employee health, number ofhours worked, environmental, social and governance (ESG) criteria,reduction in employee accidents, societal objectives, etc. As discussedherein, objectives may be unitized (measured) in terms of per shift, perday, per week, per months, per year, per an amount of currency, per anamount of morale, per an amount of a measure of health, etc. As shown inFIG. 107 , embodiments may use an agglomerate network 10100, e.g.,multiple neural networks and/or modules/models 10102, 10104, 10106,10108 configured together via connectors 10110, 10112, 10114, asdisclosed herein, to generate schedule data 10116, e.g., data defining,in part, or otherwise corresponding to, a schedule and/or aspectsthereof, wherein the various modules/circuits/models within theagglomerate network adjust the schedule data, through one or more cyclesas disclosed herein, so as to structure the schedule data to improve oneor more company/entity objectives. In embodiments, the schedule data10116 may correspond to a work schedule that defines one or more shiftsfor one or more workers, e.g., employees, of an entity, e.g., abusiness/corporation.

Accordingly, referring to FIG. 108 , an apparatus 10200 for extendedhorizon scheduling is shown in accordance with an embodiment of thecurrent disclosure. The apparatus 10200 may be embodied via one or moreprocessors on one or more electronic devices, e.g., servers,workstations, smart devices, etc. In embodiments, the apparatus 10200may form part of an agglomerate network, as disclosed herein. Inembodiments, the apparatus 10200 may be apart from an agglomeratenetwork, e.g., a standalone device that can interact with other devices.As shown in FIG. 108 , the apparatus 10200 includes an objectiveinterpretation circuit 10202, a schedule generation circuit 10204,and/or a schedule provisioning circuit 10206. As will be understood,embodiments of the apparatus 10200 may include additional circuitsdisclosed herein. The objective interpretation circuit 10202 may bestructured to interpret objective data 10208, the schedule generationcircuit 10204 may be structured to generate schedule data 10210 based atleast in part on the objective data 10208, and the schedule provisioningcircuit 10206 may be structured to transmit the schedule data 10210. Theobjective data 10208 may be data defining and/or otherwise correspondingto one or more corporate/entity objectives, e.g., reducing turnover, asdisclosed herein. In embodiments, the objective data 10208 may be a setof one or more enumerated types, each corresponding to one of aplurality of long-term corporate/entity objectives and paired to adesired value (which may be a single value and/or a range of values).For example, the objective data 10208 may include a “turnover” typepaired to a desired range of 0-10%, and a “predicted number of sales”type paired to a desired minimum threshold of $1500/month.

Shown in FIG. 109 is a method 10300 for extended horizon scheduling, inaccordance with an embodiment of the current disclosure. The method10300 may be performed by the apparatus 10200 (FIG. 2 ) and/or any othercomputing device disclosed herein. The method 10300 may includeinterpreting objective data 10302, generating schedule data based atleast in part on the objective data 10304, and transmitting the scheduledata 10306.

Turning to FIG. 110 , generating the schedule data 10304 may includeanalyzing the schedule data 10402, and, responsive to the analyzing,predicting one or more values corresponding to the corporate/entityobjectives 10404. Generating the schedule data 10304 may further includecomparing the predicted values to desired values 10406 and, responsiveto the comparing, adjusting the schedule data 10408, which may includedirectly adjusting the schedule data 10410 and/or adjusting one or morecircuits/modules/models used to generate the schedule data via one ormore connectors 10412, as disclosed herein. In embodiments, the desiredvalues may be generated by a user, by another circuit, and/or retrievedfrom a database. In embodiments, analyzing the schedule data 10402 mayinclude determining one or more properties of the schedule data, e.g., anumber and/or timing of shifts, assigned workers, estimated number ofsales, estimated profits, estimated costs in wages, and/or other typesof data related to a schedule.

Illustrated in FIG. 111 is another apparatus 10500 for extended horizonscheduling, in accordance with an embodiment of the current disclosure.The apparatus 10500 may form part of an agglomerate network 10100 (FIG.107 ) or be apart from an agglomerate network. The apparatus 10500 mayinclude a schedule interpretation circuit 10502, an objectiveinterpretation circuit 10504, a schedule trend analysis circuit 10506, ahorizon objective analysis circuit 10508, and/or a promotive actionprovisioning circuit 10510. The schedule interpretation circuit 10502 isstructured to interpret schedule data 10512. The objectiveinterpretation circuit 10504 is structured to interpret objective data10514, which, as disclosed herein, may define in part and/or otherwisecorrespond to one or more entity/corporate objectives. The scheduletrend analysis circuit 10506 is structured to extract a trend 10516 fromthe schedule data 10512. The horizon objective analysis circuit 10508 isstructured to determine whether the extracted trend 10516 furthers orimpedes an objective defined, in part, by the objective data 10514. Thehorizon objective analysis circuit 10508 is further structured to,responsive to a determination that the extracted trend 10516 impedes theobjective, generate a promotive action command value 10518 structured totrigger an adjustment to the schedule data 10512. In embodiments, theadjustment is structured to mitigate the extracted trend 10516 fromimpeding the objective. The promotive action provisioning circuit 10510is structured to transmit the promotive action command value 10518. Forexample, in embodiments, the trend 10516 may indicate that a generatedschedule, e.g., schedule data 10512, will result in a particularemployee earning too much overtime, where the adjustment may eitherdirectly remove shifts from the employee and/or adjust the bias of aconnector that generates an affect in another circuit to reduce theamount of overtime the employee is scheduled for.

In embodiments, the extracted trend 10516 may correspond to one or moreof the objectives of the objective data 10514. For example, theextracted trend 10516 may correspond to a number of hours worked, and/orover time worked. In embodiments, the extracted trend 10516 may have aunitization of per day, week, month, year, etc. In embodiments, theextracted trend 10516 may correspond to a single employee or agroup/plurality of employees. In embodiments, the trend 10516 may beextracted from the trend by identifying one or more properties withinthe schedule data 10512 and correlating the extracted properties toknown patterns of historic schedule data, which may be retrieved from adatabase and/or other data source. In embodiments, prior scheduleshaving patterns matching, to include closely matching, the trend mayalso have corresponding biases for one or more agglomerate networkmodules/circuits that can be used to adjust the connectors, as disclosedherein. In embodiments, extracting the trend 10516 may include summing aparticular schedule property over one or more portions of a schedulecorresponding to the schedule data 10512.

In embodiments, the horizon objective analysis circuit 10508 maydetermine that the extracted trend impedes an objective ifexecution/implementation of the schedule would decrease the likelihoodof, and/or prevent, the corresponding entity/corporation from achievingthe objective. For example, the extracted trend 10516 may indicate thatan initial draft of a schedule defined, in part by the schedule data, islikely to improve sales but also increase the likelihood of employeesassigned to the schedule resigning. The horizon objective analysiscircuit 10508 may determine that such an extracted trend impedes acorporate objective of reducing turnover by 5% for the month.

The promotive action command value 10518 may be structured to directlychange/adjust the schedule data 10512 to improve the extracted trend10516 so as to improve the likelihood of the entity/corporationachieving the long-term objective if the schedule isexecuted/implemented/followed. In embodiments, improving the extractedtrend 10516 may include slowing the extracted trend 10516, e.g., theobjective may be based at least in part on a slowing of the extractedtend; for example, slowing down a rate of increase for monthly travelexpenses to meet a long-term objective of staying below a given travelexpense amount. In embodiments, improving the extracted trend 10516 mayinclude increasing the extracted trend 10516, e.g., the objective may bebased at least in part on an increase in the extracted trend; forexample, increasing a projected number of monthly sales to hit along-term sales objective. In embodiments, improving the extracted trend10516 may include reversing the extracted trend 10516, e.g., theobjective may be based at least in part on reversing the extractedtrend; for example, increasing shifts lengths to reduce a total numberof employees while increasing sales so as to reverse cashflow fromnegative to positive so as to achieve a long-term corporate objective ofbecoming profitable.

Turning to FIG. 112 , in embodiments, the promotive action command value10518 corresponds to an alert message 10602. In embodiments, the alertmessage 10602 may be a visual and/or audio message. For example, thealert message 10602 may be a pop-up on an electronic display, a lightsource such as a flashing LED, a text message on a mechanical display,etc. The alert message 10602 may convey an audio message, include asound encoding, etc. In embodiments, the alert message 10602 may conveyto a user, e.g., a machine intelligence and/or a human user, one or morerecommendations for adjusting the schedule data 10512. Such adjustmentsmay include direct manipulation of the schedule data, e.g., moving ashift, assigning an employee to a shift, reducing a shift duration,adding a shift, deleting a shift, etc. Such adjustments may also includemanipulation of one or more circuits/modules within an agglomeratenetwork that generated the schedule data, e.g., adjusting a bias of aconnector, as disclosed herein. For example, the adjustment may changethe input and/or output weights of a connector. For example, anagglomerate network module/circuit that is tasked with increasing theproductivity of a schedule and generates and/or adjusts schedule data tohave long shifts to reduce a number of employees may have its associatedoutput biases adjusted down such that the weighting of the agglomeratenetwork model is weakened when received/interpreted by subsequentagglomerate network modules/circuits.

In embodiments, the schedule trend analysis circuit 10506 may include anextraction circuit 10604 structured to extract trends from the scheduledata 10512 via machine learning. One non-limiting example of such amachine learning includes a neural network that has been trained on alabeled data set to detect trends within schedule data based on one ormore objectives. For example, in embodiments, the neural network mayhave been trained to distinguish portions of schedule data that relateto a provided objective from portions that do not relate to theobjective, wherein the portions of the schedule data that do relate tothe objective are passed to the horizon objective analysis circuit10508.

In embodiments, the horizon objective analysis circuit 10508 may includea historical data interpretation circuit 10606 structured to interprethistorical schedule data 10608. The historical schedule data 10608 mayinclude data corresponding to past schedules for the entity/organizationtrying to achieve an objective of the objective data 10514. Inembodiments, the horizon objective analysis circuit 10508 may retrievethe historical schedule data 10608 via a query (for a database)generated based at least in part on the objective data 10514. Forexample, the horizon objective analysis circuit 10508 may retrievehistorical schedule data for schedules corresponding to an employee todetermine how many hours the employee worked outside of the schedulecorresponding to the schedule data 10512 being evaluated. Inembodiments, the historical schedule data 10608 may provide informationregarding an employee's past attendance rate which may provide forpredicting a future attendance rate for one or more shifts in theschedule data 10608.

In embodiments, the apparatus 10500 may include an objective rankingcircuit 10610 structured to rank one or more objectives, defined inpart, by the objective data 10514. In embodiments, the objective rankingcircuit 10610 may form part of the horizon objective analysis circuit10508. In embodiments, the objective ranking circuit 10610 may rank theobjectives based at least in part on artificial intelligence, e.g., aneural network trained on a labeled data set with the goal ofprioritizing/ranking the objectives to maximize an overall objective,e.g., corporate profitability measured in a currency, e.g., dollars. Inembodiments, the neural network may be trained to rank objectives via adata training set that labels each of a plurality of objectives with acorresponding importance value. In embodiments, the objective rankingcircuit 10610 may determine that reducing turnover ranks higher, e.g.,is more important, than reducing a number of new hires due to a recenttrend of key workers, e.g., those with significant experience, leavingthe entity/corporation.

In embodiments, a current generation of a schedule (one recentlygenerated and being evaluated by the apparatus 10500) may be compared toone or more prior schedules, e.g., historical data, that have knownresults with respect to an objective, e.g., the effects of priorschedules on an objective may be known as the prior schedules wereexecuted/followed/implemented. Such comparing may be used to adjust oneor more confidences associated with the current generation schedule,e.g., a confidence value/level that the current generation scheduleresults in a particular range of sale. As described in greater detailherein, in embodiments, the current generation schedule may be adjustedif a confidence level is below a threshold.

Shown in FIG. 113 is another method 10700 for extended horizonscheduling, in accordance with embodiments of the current disclosure.The method 10700 may be performed by the apparatus 10500 (FIGS. 105 and106 ) and/or any other computing device disclosed herein. The method10700 may include: interpreting schedule data 10702; interpretingobjective data 10704; and extracting a trend from the schedule data10706. The method 10700 may further include determining whether theextracted trend furthers or impedes an objective defined, in part, bythe objective data 10708; and responsive to a determination that theextracted trend impedes the objective, generating a promotive actioncommand value structured to trigger an adjustment to the schedule data,wherein the adjustment is structured to mitigate the extracted trendfrom impeding the objective 10710. The method 10700 may further includetransmitting the promotive action command value 10712.

Turning to FIG. 114 , in embodiments, the method 10700 may furtherinclude adjusting the schedule data in response to the promotive actioncommand value 10802. In embodiments, adjusting the schedule data mayinclude slowing the extracted trend 10804. For example, the extractedtrend 10804 may be a three-time increase in a number of workers workinga single shift, as compared to a prior historic schedule, wherein theadjustment to the schedule data may be a reduction to a two-timeincrease in the number of workers working the single shift. Inembodiments, adjusting the schedule data may include increasing theextracted trend 10806. For example, the extracted trend 10804 may be ashrinking pay gap for a particular shift above industry norms, and theadjustment may be to increase the wage for the shift with the intent ofretaining high quality employees. In embodiments, adjusting the scheduledata may include reversing the extracted trend 10808. For example, theextracted trend 10804 may be an increase in a number of hours for aparticular worker, where the increase is on track to exceed a desiredmaximum number of hours/week, and the adjustment may be to adjust theschedule data such that the employee has fewer hours than the priorweek. In embodiments, adjusting the schedule data in response to thepromotive action command value 10802 may include directly changing ashift in the schedule data in response to the promotive action commandvalue 10810. In embodiments, adjusting the schedule data in response tothe promotive action command value 10802 may include adjusting aconnector 10812.

Referring to FIG. 115 , an agglomerate network 10900 having an extendedhorizon evaluation circuit 10902, e.g., apparatuses 10200 (FIG. 108 )and 10500 (FIG. 111 ), is shown. The agglomerate network 10900 may alsoinclude a scheduler circuit 10904 and one or more connector circuits10906. In embodiments, the agglomerate network 10900 may further includeother types of agglomerate network circuits disclosed herein.

The scheduler circuit 10904 is structured to output schedule data 10908.As disclosed herein, the schedule data 10908 includes data correspondingto a schedule, e.g., a Gantt representation of a schedule, data relatedto the employee(s) and/or scheduled entities, a total number of hours,etc. The one or more connector circuits 10906 may be structured toadjust at least one of an input 10910 to the scheduler circuit 10904and/or the schedule data 10908 outputted by the scheduler circuit 10904.Adjusting of a connector circuit 10906 may include adjusting one or morebiases and/or the circuits to which the connector circuit 10906connects, as disclosed herein. The extended horizon evaluation circuit10902 may be structured to: interpret the schedule data 10908, interpretobjective data 10912, and extract a trend, as disclosed herein, from theschedule data. The extended horizon evaluation circuit 10902 may befurther structured to determine whether the extracted trend furthers orimpedes an objective defined, in part, by the objective data, asdisclosed herein. Upon determining that the extracted trend impedes theobjective, the extended horizon evaluation circuit 10902 may thengenerate a promotive action command value 10914 that is structured totrigger an adjustment to one or more of the connector circuits 10906 toeffect a change of at least one of the input 10910 to the schedulercircuit 10904 or the schedule data outputted 10908 by the schedulercircuit 10904 such that the extracted trend is mitigated from impedingthe objective. The extended horizon evaluation circuit 10902 may thentransmit the promotive command value 10914. Transmission of thepromotive command value 10914 may include storing the promotive commandvalue 10914 in a memory location accessible to the event horizonevaluation circuit 10902, the connector circuits 10906, and/or any otheragglomerate network circuit in the agglomerate network 10900. Inembodiments, the event horizon evaluation circuit 10902 may directlychange the connector circuits 10906 without having to transmit apromotive command value 10914. For example, the event horizon evaluationcircuit 10902 may have access to memory storing values, e.g., biases,used by the connector circuits 10906.

As shown in FIG. 116 , in embodiments, the agglomerate network 10900 mayfurther include a schedule adjuster circuit 11002 structured to receivethe schedule data 10908 from the connector circuit 10906, where theschedule adjuster circuit 11002 adjusts the schedule data 10908.

In embodiments, the agglomerate network circuit 10900 may includeanother connector circuit 11004 structured to receive adjusted scheduledata 11006 from the schedule adjuster circuit 11004 and bias theadjusted schedule data 11006. In embodiments, the adjustment to theconnector circuit 11004 may change a bias of the connector circuit11004. In embodiments, the one or more connector circuits 10906 and/or11004 may adjust the input to the scheduler circuit 10904. Inembodiments, the one or more connector circuits 10906 and/or 11004adjust the schedule data 10908 outputted by the scheduler circuit 10904.

In embodiments, the schedule circuit 10904 may feed outputted scheduledata 10908 directly into the event horizon evaluation circuit 10902along with one or more goals, e.g., objectives 10912, as shown in FIG.116 . In other embodiments, the schedule circuit 10908 may be locateddownstream of the schedule circuit 10904 such that one or moreintermediate agglomerate network circuits are disposed in between theschedule circuit 10904 and the event horizon circuit 10902. Inembodiments, the objective may be to maintain above a desired minimumthreshold for a 1-year rolling retention rate, e.g., 96%.

In embodiments, the agglomerate network 10900 may include a schedulewarden circuit, e.g., 20100 (FIG. 37 ), as disclosed herein, and/or theevent horizon evaluation circuit 10902 may be integrated with a schedulewarden circuit, e.g., the extended horizon evaluation circuit mayevaluate schedules within an agglomerate network 10900 to improve theodds of achieving a company's long-term objectives and/or to ensure thatthe scheduling norms (customary and/or legal) are not violated. Forexample, schedule data 10908 that has been evaluated by the eventhorizon evaluation circuit 10902 may be transmitted to the schedulewarden circuit for further evaluation and/or adjustments, as disclosedherein.

In embodiments, the agglomerate network 10900 may include two or morescheduler circuits 10904 and 11008 that may compete against each otherto see which can be the first to generate schedule data 10908, e.g., aschedule, that satisfies the event horizon evaluation circuit 10902first and/or in the most suitable manner.

Accordingly, as disclosed herein, embodiments of the event horizonevaluation circuit, e.g., apparatuses 10200, 10500, and/or 10902, mayreceive inputs, e.g., schedule data and/or other data, e.g., biases,objective data, etc., as: direct inputs, i.e., event horizon evaluationcircuit may be a standalone module/circuit; as direct input to anagglomerate network, i.e., without use of connectors; and/or fromconnectors, i.e., the event horizon evaluation circuit 10902 is one of aplurality of modules/circuits/models within an agglomerate network. Forexample, in embodiments, the event horizon evaluation circuit 10902 mayevaluate a schedule generated by a scheduling circuit/module (within anagglomerate network) against a goal and generate an output, e.g., arating/score, bias, etc. The event horizon evaluation circuit 10902 may,in turn, feed the output back into the scheduling circuit/module to forma feedback loop which tries to reach equilibrium and/or optimization ofvarious biases in the agglomerate network. The connections between theevent horizon evaluation circuit and the various modules of theagglomerate network may be accomplished via connectors, as disclosedherein.

Illustrated in FIG. 117 is another apparatus 11100 for extended horizonscheduling, in accordance with embodiments of the current disclosure.The apparatus 11100 includes a schedule interpretation circuit 11102, anobjective interpretation circuit 11104, a schedule trend analysiscircuit 11106, a horizon objective analysis circuit 11108, and/or apromotive action provisioning circuit 11110. The schedule interpretationcircuit 11102 may be structured to interpret schedule data 11112, theobjective interpretation circuit 11104 may be structured to interpretobjective data 11114, and the schedule trend analysis circuit 11106 maybe structured to extract a trend 11116 from the schedule data 11112. Thehorizon objective analysis circuit 11108 may be structured to interpreta score 11118, e.g., a baseline score, of the extracted trend 11116. Inembodiments, the baseline score 11118 may correspond to an objective11120 defined, in part, by the objective data 11114. The horizonobjective analysis circuit 11108 may score 11122 the extracted trend11116 with respect to the objective 11120 and compare the score 11122 tothe baseline score 11118 to determine a distance 11124, e.g., adifference in value, between the score 11122 and the baseline score11118. The horizon objective analysis circuit 11108 may then generate apromotive action command value 11126 structured to trigger an adjustmentto the schedule data 11112 that is structured to change the distance11124. The promotive action provisioning circuit 11110 may be structuredto transmit the promotive action command value 11126. While theforegoing example concerned comparing the score 11122 to a baselinescore 11118, it should be understood that the score 11122 may becompared to other types of scores other than baseline ones, e.g., thescore 11118 may be a user defined score, an intended target score, anindustry defined score, and/or any other type of score.

Turning to FIG. 118 , in embodiments, the horizon objective analysiscircuit 11108 may be further structured to score the extracted trend11116 based at least in part on machine learning. For example, thehorizon objective analysis circuit 11108 may include a scoring circuit11202 that performs the scoring 11122. In embodiments, the scoringcircuit 11202 may use a neural network to score the extracted trend11116. The neural network may be trained with respect to scoring theextracted trend 11116 with respect to a specific objective, e.g., 11120,or with respect to a plurality of objectives. In embodiments, the neuralnetwork may be trained on a training data set that includes trend datalabeled with respect to the objective, e.g., 11120.

In embodiments, the objective data 11114 defines, in part, anotherobjective 11204 and the promotive action command value 11126 seeks tooptimize both objectives 11120 and 11204. In embodiments, bothobjectives 11120 and 11204 may be ranked by at least one of either anartificial intelligence, or a human user. For example, the horizonobjective analysis circuit 11108 may include a ranking circuit 11206structured to rank the objectives based on a set of rules generated by auser or by an artificial intelligence. The set of rules may bestructured to rank objectives so as to achieve another objective, e.g.,profitability.

In embodiments, the adjustment triggered by the promotive action commandvalue 11126 may be structured to increase the distance 11124. Inembodiments, the adjustment triggered by the promotive action commandvalue 11126 may be structured to decrease the distance 11124.

FIG. 119 shows a method 11300 for extended horizon scheduling, inaccordance with embodiments of the current disclosure. The method 11300may be performed by the apparatus 11100 and/or any other computingdevice disclosure herein. The method 11300 may include interpretingschedule data 11302 and interpreting objective data 11304. The method11300 may further include extracting a trend from the schedule data11306 and interpreting a baseline score of the extracted trend 11308. Asdisclosed herein, in embodiments, the baseline score may correspond toan objective defined, in part, by the objective data. The method 11300may include scoring the extracted trend with respect to the objective11310. The method 11300 may include comparing the score to the baselinescore to determine a distance between the score and the baseline score11312. In embodiments, the method 11300 may include generating apromotive action command value structured to trigger an adjustment tothe schedule data 11314. As disclosed herein, the adjustment may bestructured to adjust the schedule data to change the distance. Themethod 11300 may further include transmitting the promotive actioncommand value 11316.

Referring to FIG. 120 , in embodiments, scoring the extracted trend withrespect to the objective 11310 may be based at least in part on machinelearning. As such, the method 11300 may include training a neuralnetwork to score the extracted trend 11402. In embodiments, the trainingset for the neural network may include trend data labeled with anobjective. As disclosed herein, in embodiments, the objective data maydefine, in part, another objective. In such embodiments, the method11300 may further include optimizing both objectives in response to thepromotive action command value 11404.

The systems and methods described herein for extended horizon schedulingprovide various technical benefits. In one aspect, the systems andmethods provide for efficient utilization of computing resources therebyreducing computation time and computation resources to automate thegeneration of schedules that meet desired extended horizon objectives.The system and methods enable efficient utilization of resources byadapting and configuring trained models to changing data and trends withreduced requirement to retrain the schedule models for different datatrends and changing data. The system and methods enable adaptation ofmodels to trends and changes without requiring models that areexplicitly trained for the trends and data changes. In one example,promotive action commands and biasing connectors enable adaptation oftrained models to new data thereby reducing the resource and timeintensive task of frequent retraining of models.

In another aspect, the methods and systems improve the training ofmodels and reduce the complexity of trained models for extended horizonscheduling. Embodiments of the system and methods described herein mayleverage a plurality of models that are trained for a plurality ofsimpler more specific tasks rather than utilizing one trained model forthe complete task. The use of a plurality of simpler trained modelsreduces the data requirements for training and increases the confidenceof the models for each task for the available data. In one example, atrained schedule trend circuit may be used to detect trends and providefeedback to another scheduling circuit. This example configurationenables the use of two separately trained and simpler models that, usingfeedback can generate schedules for extended horizon scheduling withreduced training data requirements.

In embodiments, a summary agglomerate model, e.g., a division levelresource planner, may combine the outputs of multiple project schedulersinto a single resource plan. As part of this activity, the divisionlevel resource planner may allow users to input dependency data such asProject A must be completed before Project D can commence. Additionally,the system may augment user entered dependencies with dependenciesdeveloped from the use of shared resources such as personnel andequipment needs.

Accordingly, embodiments of the current disclosure provide for systemsand methods, which may form part of modules/components 124, 126, and/or128, (FIG. 1 ), for adjusting a schedule in response to austere events.For example, embodiments of the current disclosure may provide for anaustere environment scheduler that uses an agglomerate network tooptimize a schedule to deal with a short-handed staff or difficultsituation, e.g., equipment malfunction, material shortages. Embodimentsof the austere environment scheduler may provide for “in the moment”,e.g., real-time and/or near real-time, operational changes in abusiness. Non-limiting examples of such changes include reallocationand/or redistribution of staff and/or detection of staff shortages inview of a company/business goal. As will be understood, austereenvironments may include low-margin and/or high-volume businesses, e.g.,retail. As used herein, an austere event includes an unanticipated eventthat can adversely affect the efficiency and/or operation of an entity,e.g., a corporation.

For example, staffing shortages may develop when a workload is greaterthan originally anticipated or if one or more scheduled employees areunable to report for an assigned shift. As overscheduling employees fora shift can be costly and inefficient, a well-designed schedule willtypically not assign more employees than is required to cover ananticipated or predicted workload for a shift period. As such, when anunanticipated staffing shortage arises due to an austere event—forexample, but not limited to, if an employee calls in sick or externalfactors significantly increase the number of customers to be servedduring a given shift—the efficiency of a function for a business, e.g.,a business operation, can, in certain situations, suffer. In addition tostaffing shortages, other austere events can impact the efficiency of awork environment. Such austere events include, for example, but are notlimited to, malfunctioning equipment, supply and material shortages,delays in receiving equipment, weather related events (such as, but notlimited to, snowstorms, floods, inclement weather, or unseasonableweather), shortages of personnel, and traffic related events. In manysituations, the impact of such events on the efficiency of a function ofa business can be eliminated or otherwise mitigated with scheduleadjustments. For example, the increased production time caused by amalfunctioning piece of equipment could, in certain circumstances, beeliminated or otherwise mitigated by assigning additional employees tothe current shift to increase the overall throughput of the function ofthe business. For many functions of businesses, the scheduling issuesintroduced by austere events, which often occur with little or nonotice, usually need to be immediately addressed or the efficiency offunction of the business will suffer—thereby negatively impactingprofits and production.

To address the impact of austere events on a function of a business, theembodiments of the current disclosure provide for an austere eventscheduling system for responding to such events by making real-timeschedule adjustments or adjustments to future schedules in order toeliminate or otherwise mitigate the impact of such austere event onfunctions of businesses. In general, according to the methods of thepresent disclosure, this austere event scheduling system includes aschedule interpretation circuit structured to access, store, andinterpret work schedules for a function of the business and to provideschedule data to other circuits within the system. In embodiments, theschedule interpretation circuit includes elements that actively monitorschedules stored in other systems. Within other aspects of the presentdisclosure, schedules are loaded directly into the scheduleinterpretation circuit. The schedule interpretation circuit may, incertain embodiments, include a computer including a microprocessorand/or some other electronic digital logic circuit.

In certain aspects of the present disclosure, austere event data, e.g.,data indicating the occurrence and details of a currently occurring orpredicted future austere event, is supplied directly to the austereevent scheduling system. In other aspects of the present disclosure, theaustere event scheduling system also includes an external eventinterpretation circuit and an austere event detection circuit which canbe used to monitor external event data (as well as schedule datainterpreted by the schedule interpretation circuit) to detect or predictthe occurrence of an austere event. For example, within such aspects theexternal event interpretation circuit may include an element with accessto the internet that monitors news data sources and other sources ofinformation, e.g., external databases. In another example, within suchaspects the external event interpretation circuit may include elementsfor receiving data on scheduled employees such that an employee callingin sick or being stuck in traffic is automatically sensed by themitigation circuit or external event interpretation circuit. Suchexternal event data can include both real time and forecasted weatherdata, traffic data, and news data. Such external event data can alsoinclude employee data, such as, but not limited to, information onemployee health events, e.g., if an employee reports an illness, newmedical condition, or medical emergency, and employee life events, e.g.,special milestone events such as birthdays, anniversaries, or the birthof a child. Such external event data can also include business functiondata such as, but not limited to, supply chain information, e.g.,components low in stock or on backorder, and the status of equipment,e.g., if a machine is offline, malfunctioning, or due for maintenance.Within certain aspects of the present disclosure, the external eventinterpretation circuit includes elements that actively monitor datastored in other systems. Within other aspects of the present disclosure,such external data as described above is loaded directly into theexternal event interpretation circuit. The external event interpretationcircuit may, in certain embodiments, include a computer includingmicroprocessor or some other electronic digital logic circuit.

Within certain aspects of the present disclosure, the austere eventscheduling system further includes an austere event detection circuitcapable of processing the external event data interpreted by theexternal event interpretation circuit and schedule data interpreted bythe schedule interpretation circuit to determine if an austere event ispresently occurring or is likely to occur in the near future. In someaspects of the present disclosure, the austere event scheduling systemincludes a machine learning component, which is trained on sets oftraining data to predict or detect an austere event responsive to thesupplied external event or schedule data. The machine learning componentcan include an artificial intelligence circuit or a neural network, andthe training data can include data sets associating prior austere eventswith indicators found in prior schedule data and prior external eventdata wherein such indicators were predictive of an austere event. Incertain aspects of the present disclosure, the machine learningcomponent can be trained to recognize trends in schedule data. Suchtrends include, but are not limited to, overuse of a piece of equipment,the number and timing of vacations, and a shrinking resource pool ofresources, e.g., the number of trucks used in long distance trips, thenumber of batches assigned to a limited number of stations, and themachines available to process work orders. By identifying such trends inadvance, embodiments of the austere event detection circuit can predictthe occurrence of an austere event prior to its impact on the functionof a business. The austere event data interpretation circuit may, incertain embodiments, include a computer including a microprocessorand/or some other electronic digital logic circuit. Within certainaspects of the present disclosure that include an external eventinterpretation circuit and an austere event detection circuit asdescribed above, upon detection of an austere event, the austere eventdetection circuit may generate austere event data indicative of thedetected austere event. Within other aspects of the present disclosure,austere event data may be provided directly to the austere eventscheduling system.

In embodiments, the austere event scheduling system further includes amitigation circuit capable of triggering an adjustment to a scheduleresponsive to austere event data that is indicative of an austere event.As described in detail herein, this austere event data can be providedto the austere event scheduling system, or it can be generated withinthe austere event scheduling system responsive to external event dataand schedule data. In either case, responsive to an indication of anaustere event, embodiments of the mitigation circuit process scheduledata provided by the schedule interpretation circuit and determine ifthe indicated austere event will result in a significant impact on thefunction of a business. If so, embodiments of the mitigation circuit maythen determine if an adjustment to the schedule will eliminate and/orotherwise mitigate the impact of the austere event on the function ofthe business. If such an adjustment is found, embodiments of themitigation circuit may then generate a mitigation action command valueindicative of the schedule adjustment. In some aspects of the presentdisclosure, embodiments of the mitigation circuit include elements toanalyze schedule data along with austere event data to predict whetherthe occurrence of an austere event that will impact a function of abusiness. Within such aspects, embodiments of the mitigation circuit arecapable of issuing a mitigation action command in advance of the austereevent, allowing the schedule change to take effect prior to theoccurrence of the event.

The mitigation circuit may, in certain embodiments, include a computerincluding a microprocessor or some other electronic digital logiccircuit. Within certain aspects of the present disclosure, themitigation circuit includes an artificial intelligence element, amachine learning element, or a neural network. In such aspects of thepresent disclosure, the mitigation circuit is trained on one or moretraining data sets to develop an artificial intelligence system todetermine a schedule adjustment responsive to a detected austere eventas described above. The training set of data may include dataassociating past austere events with scheduling changes thatsuccessfully eliminated or otherwise mitigated the impact of the austereevent on the operation of a business. In certain aspects of the presentdisclosure, the mitigation circuit uses logic algorithms executed on oneor more processors to determine schedule adjustments. In certain aspectsof the present disclosure, the mitigation circuit uses a look up tableof austere events or categories of austere events associated withschedule changes predetermined to eliminate or otherwise mitigate theimpact of the austere events on functions of businesses.

Embodiments of the austere event scheduling system may further include amitigation action provisioning circuit capable of transmitting themitigation action command value to other systems. In this way, otherscheduling systems are able to, responsive to the austere eventscheduling system, execute a change to a schedule to eliminate orotherwise mitigate the effect of an austere event. In certain aspects ofthe present disclosure, the mitigation action command value takes theform of an alert. In other aspects of the present disclosure, themitigation action command value further includes a recommended schedulechange and/or a reallocation of resources (for example, but not limitedto, the assignment of additional staff to a shift, the allocation ofadditional resources (e.g., materials, products for sale, equipment oremployees), or the scheduling of time on a previously unscheduledmachine).

Embodiments of an austere event scheduling system may be amodule/model/circuit that receives inputs, e.g., a schedule and/or otherdata, e.g., biases, as: direct input, e.g., the austere event schedulingsystem acts as a standalone module/model/circuit; as direct input to anagglomerate network, e.g., without use of connectors; from connectors,e.g., the austere event scheduling system is one of a plurality ofmodules within an agglomerate network; and the like. For example, theaustere event scheduling system may be a module/model/circuit within anagglomerate network that evaluates a schedule, generated by a humanand/or a scheduling module, against trends extracted from the effects ofaustere events on prior schedules and generates an output, e.g., a bias.The austere event scheduling system module/model/circuit may, in turn,feed the output back into the scheduling module (or back to the human asa message) to form a feedback loop which tries to reach equilibriumand/or optimization of various biases in the agglomerate network. Theconnections between the austere event scheduling systemmodule/circuit/model and the various modules/models/circuits of theagglomerate network may be accomplished via connectors, as disclosedherein.

Embodiments of the austere event scheduling system of the presentdisclosure will now be described in more detail within the discussion ofFIGS. 121-130 .

Referring to FIG. 121 , an embodiment of an austere event schedulingapparatus 40101 is provided. The apparatus 40101 includes a scheduleinterpretation circuit 40102 structured to interpret schedule data40133. As discussed herein, dependent on the needs of the application,within some embodiments schedule data 40133 can be provided directly tothe apparatus 40101 or, within other embodiments, the scheduleinterpretation circuit 40102 is structured to monitor schedule data40133 from outside the apparatus 40101. The apparatus further includes amitigation circuit 40108 which is structured to generate, responsive tointerpreted schedule data (provided by the schedule interpretationcircuit 40102) and austere event data 40137 (provided to the apparatus40101), a mitigation action command value 40145. As discussed in detailherein, the mitigation action command value 40145 may be structured toeffect a change in schedule data sufficient to mitigate the effect of anaustere event on a function of a business. The mitigation circuit 40108may process the interpreted schedule data (as provided by the scheduleinterpretation circuit 40102) and the austere event data 40137 (e.g.,weather information, traffic information, details on equipment status,employee information) to detect the occurrence of an austere eventlikely to adversely impact the function of the business. In some cases,the austere event may be happening concurrently with the detection, mayhave happened in the past, and in other cases, the mitigation circuit40108 predicts an austere event which may happen in the future. Ineither case, if such an austere event is detected, the mitigationcircuit 40108 may generate a mitigation action command value 40145. Thismitigation action command value 40145 may be indicative of a schedulechange that will eliminate or otherwise mitigate the effect of adetected austere event. Finally, the apparatus 40101 includes amitigation action provisioning circuit 40112 which is structured totransmit the mitigation action command value 40145 to systems externalto apparatus 40101. Responsive to the transmitted mitigation actioncommand value 40150, these external systems can implement the schedulechange generated by the mitigation circuit 40108 and thereby eliminateor otherwise significantly mitigate the impact of a detected austereevent.

One non-limiting use case of the apparatus 40101 includes a scenariowhere an unexpected overnight snowstorm results in an employee scheduledto work a shift being unable to make it to work on time due to a knownlong work commute through mountainous terrain. Embodiments of theapparatus 40101 may be able to detect the severity of the snowstorm inreal-time, or near real-time, while the employee and associated managerare asleep. As such the apparatus 40101 may predict the inability of theemployee to make it to work before the employee and manager wake themorning of the shift, identify another employee who can walk to thelocation of the shift, generate a schedule that assigns the anotheremployee to cover the shift, and transmit a message, e.g., text messageand/or e-mail, to the another employee to let them know of theassignment. Embodiments of the apparatus 40101 may work in conjunctionwith a schedule warden circuit, e.g., 20100 (FIG. 37 ), to ensureassignment of other employee to the shift does not violate schedulenorms, as disclosed herein. Embodiments of the apparatus 40101 may alsowork in conjunction with an incentive scheduling circuit, e.g., 180100(FIG. 8 ), as disclosed herein, to incentivize other employee to workthe newly assigned shift on a last-minute notice.

Another non-limiting use case of the apparatus 40101 includes a scenariowhere the apparatus 40101 detects that a snowstorm is expected to hit intwo-days' time, and then determines that a store will be understaffed tohandle an expected influx of customers planning to stock up on goods,e.g., milk and eggs. The apparatus 40101 may generate a schedule thatassigns additional employees to work shifts at the store prior to thesnowstorm hitting and then send messages out to the additional employeesand any associated managers to inform them of the schedule. As will beunderstood, by automating the process of detecting austere events, e.g.,staffing shortages due to unexpected snowstorms, embodiments of thecurrent disclosure can generate schedules in timely manner so as toprovide affected workers improved notice, which in turn, may reduce anentity's turnover rate. Further, automating the process of detectingaustere events and generated schedules to handle to detected austereevents can be accomplished during hours which many employees andmanagers are sleeping, e.g., during the night, thus mitigating the needfor employees and managers to stay up during the night to monitorweather events. Thus, in turn, embodiments of the apparatus 40101 mayprovide for better rested employees that are more efficient during theirshifts and/or less likely to get into an accident on the job or during awork commute.

In embodiments, connectors, as disclosed herein, may provide for austereevent detection and resolution (provided via the apparatus 40101) to beincorporated with/into another scheduling model that is not directlyeditable, e.g., a black box model. Thus, embodiments of the apparatus40101 provide for event detection and resolution to be incorporatedwith/into models that, on their own, are not capable of such a feature.

Looking now to FIG. 122 , another embodiment of an austere eventscheduling apparatus 40201 is provided. The apparatus 40201 includes aschedule interpretation circuit 40102, an external event interpretationcircuit 40206, and an austere event detection circuit 40204. Asdiscussed herein, the schedule interpretation circuit 40102 isstructured to interpret schedule data 40133, which, dependent on theneeds of the scenario, may be provided directly to the apparatus 40201or monitored via the schedule interpretation circuit 40102 from outsidethe apparatus 40201. The external event interpretation circuit 40206 isstructured to interpret external event data 40235, such as, but notlimited to: weather data, supply chain data, the status of equipment,employee health events, e.g., illness, acute incapacity, or healthemergencies; employee life events, e.g., birthdays, children, orsignificant personal milestones; or geo-political events, e.g.,government induced trade restrictions. As with the scheduleinterpretation circuit 40102, dependent on the needs of the scenario,the external event data 40235 may be provided directly to the apparatus40201 or the external event circuit 40206 is structured to monitorexternal event data 40235 from outside the apparatus 40201.

The interpreted schedule data and the interpreted external event datamay be provided to the austere event detection circuit 40204, which maybe structured to detect an austere event and, in response, generateaustere event data 40137 indicative of the austere event. That is, theaustere event detection circuit 40204 may process schedule information(supplied to the apparatus 40201 as schedule data 40133 and interpretedby the schedule interpretation circuit 40102) and external eventinformation (supplied to the apparatus 40201 as external event data40235 and interpreted by the external event interpretation circuit40206) and determines if an austere event is currently occurring, hasoccurred, or will occur in the future and generates austere event data40137 indicative of the austere event. Within apparatus 40201, as shownin FIG. 122 , the austere event detection circuit 40204 uses machinelearning to analyze the interpreted schedule data and the interpretedexternal event data to detect an austere event and generate the austereevent data 40137. In some aspects of the present disclosure, thismachine learning includes a neural network 40205 trained with trainingdata 40223. The training data 40223 can, in some aspects of the presentdisclosure, include data associating past austere events with priorschedule data and prior external event data. In some aspects of thepresent disclosure, the neural network 40205 is trained using trainingdata 40223 to detect trends 40207 in schedule data indicative of anaustere event. Such trends 40207 can include, but are not limited to, anoveruse of a piece of equipment, the number of employee vacationsscheduled, the timing of scheduled employee vacations, a shrinkingresource pool, and the like. For example, a neural network may learnthat a snowstorm may require a twenty percent (20%) staffing increasefor two to three days before the snowstorm is expected to hit alocation. In turn the neural network may adjust one or more biases in anagglomerate network, as disclosed herein, to increase the weights of amodel/circuit/module that calls for increased staffing in a schedule.

In embodiments, a neural network may learn that certain adjustments toan agglomerate network are viable only in certain circumstances. Forexample, in embodiments, a neural network may learn that increasedstaffing mitigates an austere event for a snowstorm in a warm location,e.g., North Carolina, where an expected snowfall is <2″ but is notwarranted in a northern location, e.g., Vermont, where snowfall ispredicted to be <4″, e.g., people in Vermont are less likely to beimpacted by snow than people in North Carolina and therefore will feelless of a need to stock up on milk and eggs prior to the storm.

Embodiments of the austere event scheduling apparatus 40201 can furtherinclude a mitigation circuit 40108, which, responsive to interpretedschedule data 40133 (provided by the schedule interpretation circuit40102) and austere event data 40137 (provided by the austere eventdetection circuit 40204), is structured to generate a mitigation actioncommand value 40145. As discussed in detail above, this mitigationaction command value 40145 is structured to effect a change in scheduledata sufficient to mitigate an effect on an austere event. Themitigation circuit 40108 may processes the interpreted schedule data40133 (as provided by the schedule interpretation circuit 40102) and theaustere event data 40137 (as provided by the austere event detectioncircuit 40204) to determine if a detected austere event is likely toadversely impact the business operation. If such a likelihood isdetermined, the mitigation circuit 40108 generates a mitigation actioncommand value 40145. The mitigation action command value 40145 may beindicative of and/or be structured to trigger a schedule change thatwill eliminate or otherwise mitigate the effect of a detected austereevent. In certain aspects of the apparatus 40201, as shown in FIG. 122 ,the mitigation circuit 40204 uses machine learning to analyze theinterpreted schedule data and the austere event data 40137 to determineif the detected austere event is likely to adversely impact businessoperation and generate the mitigation action command value 40145 inresponse. In some aspects of the present disclosure, this machinelearning includes a neural network 40209 trained with training data40225. The training data 40225 can, in some aspects of the presentdisclosure, include data associating past austere events with schedulechanges that successfully eliminated and/or otherwise mitigated theimpact of those events on business operations.

In embodiments, the austere event scheduling apparatus 40201 includes amitigation action provisioning circuit 40112 which is structured totransmit 40150 the mitigation action command value 40145 to systemsexternal to apparatus 40201. Responsive to the transmitted mitigationaction command value 40150, these external systems can implement theschedule change generated by the mitigation circuit 40108 and therebyeliminate or otherwise significantly mitigate the impact of a detectedaustere event.

Referring now to FIG. 123 , a method 40300 for adjusting a scheduleresponsive to an austere event is provided. The method 40300 may beperformed via apparatuses 40101, 40201, and/or any other computingdevice disclosed herein. As shown in FIG. 123 , at 40310, schedule datais interpreted via a schedule interpretation circuit. At 40350, amitigation action command value is generated via a mitigation circuit.The mitigation action command value may be based, at least in part, onthe schedule data as interpreted at 40310 and/or austere event datacorresponding to at least one austere event; and is structured totrigger an adjustment to the schedule data. This adjustment to theschedule data is, in turn, structured to effect a change of a propertywithin the schedule data sufficient to mitigate an effect of an austereevent corresponding to the austere event data on one or more entitiesassociated with the schedule data. At 40360, the mitigation actioncommand value may be transmitted, such as via a mitigation actionprovisioning circuit. In this way, embodiments of method 40300 eliminateor otherwise mitigate the effect(s) of the austere event on the one ormore entities. The structure and function of the schedule interpretationcircuit, the mitigation circuit, and/or the mitigation action provisioncircuit—as well as austere event data corresponding to an austere eventas used within those circuits—are described in further detail within thediscussions of FIGS. 121 and 122 , herein.

Referring now to FIG. 124 , another method 40400 for adjusting aschedule response to an austere event is provided. The method 40400 maybe performed by apparatuses 40101, 40201, and/or any other computingdevice disclosed herein. At 40310, schedule data is interpreted via aschedule interpretation circuit. At 40420, external event data isinterpreted by an external event interpretation circuit. Within certainaspects of the present disclosure, the external event data maycorrespond to one of more types of data including, but not limited to,weather data, supply chain data, equipment status data, employee healthevent data, employee life event data, geo-political event data, and/orthe like. At 40430, at least one of the interpreted schedule data or theinterpreted external event data are processed via an austere eventdetection circuit to detect an austere event. The detection at 40430 canbe performed by a neural network within the austere event detectioncircuit, wherein the neural network is trained to predict austere eventsfrom the schedule data. At 40440, if an austere event is detected at40430, austere event data corresponding to the detected austere event isgenerated by the austere event detection circuit. At 40350, a mitigationaction command value is generated via a mitigation circuit. Themitigation action command value is based, at least in part, on theschedule data (as interpreted at 40310) and on austere event data(generated at 40440) and is structured to trigger an adjustment to theschedule data. This adjustment to the schedule data may be, in turn,structured to effect a change of a property within the schedule datasufficient to mitigate an effect of an austere event corresponding tothe austere event data on one or more entities associated with theschedule data. In this way, method 40400 eliminates and/or otherwisemitigates the effect of the austere event on the one or more entities.The structure and function of the schedule interpretation circuit, themitigation circuit, and/or the mitigation action provision circuit—aswell as schedule data and external event data as used within thosecircuits—are described in detail within the discussions of FIGS. 121 and122 , herein.

Referring to FIG. 125 , an agglomerate network 40501 for generatingschedule data which includes an austere event circuit 40560 according tothe methods of the present disclosure is provided. The agglomeratenetwork 40501 for generating schedule data includes a scheduler circuit40510 structured to output the schedule data; and a connector circuit40520 structured to adjust at least one of an input to the schedulercircuit or the schedule data outputted by the scheduler circuit. Theagglomerate network 40501 further includes an austere event circuit40560 structured to: interpret 40562 the schedule data 40533; andgenerate 40564, based at least in part on schedule data 40533 andaustere event data 40537, a mitigation action command value 40545structured to trigger an adjustment to a connector 40570. The adjustmentis structured to effect a change of at least one of the input to thescheduler circuit or the schedule data outputted by the schedulercircuit. The austere event circuit 40560 is further structured totransmit 40566 the mitigation action command value 40545.

Referring to FIG. 126 , certain further aspects of the agglomeratenetwork 40501 for generating schedule data are described following, anyone or more of which may be present in certain embodiments. For example,the austere event circuit may generate the mitigation action commandvalue 40545 based at least in part on machine learning 40665. Themachine learning extracts trends 40667 from the schedule data 40533, andthe trends 40667 are used to generate the mitigation action commandvalue 40545. In embodiments, the trends 40667 include an overuse of apiece of equipment, a number of vacations, timing of vacations, ashrinking resource pool, and/or the like.

Referring now to FIG. 127 , a non-transitory computer-readable medium40700 that stores instructions that adapt at least one processor tointerpret and adjust schedule data responsive to an austere event isprovided. The instructions that adapt at least one processor to: at40710, interpret schedule data; at 40720, generate, based at least inpart on the schedule data and austere event data, a mitigation actioncommand value 40745 structured to effect a change of a property of theschedule data; and at 40730, transmit the mitigation action commandvalue.

Referring to FIG. 128 , certain further aspects of the non-transitorycomputer-readable medium 40700 are described following, any one or moreof which may be present in certain embodiments. For example, inembodiments, the mitigation action command value 40745 is structured togenerate a message to a user 40853, adjust a bias of a connector in anagglomerate network 40855, directly adjust the schedule data 40857,and/or perform some combination of these actions.

Referring to FIG. 129 , another method 40900 of adjusting a scheduleresponsive to an austere event is provided. The method 40900 includes at40910, transmitting, via a local computing device, austere event data toa scheduling platform hosted on one or more remote servers; at 40920,accessing, via the local computing device, schedule data generated viathe scheduling platform based at least in part on an austere eventcircuit; and at 40930, executing a schedule based at least in part onthe schedule data. In embodiments, the schedule is structured tomitigate an effect of an austere event corresponding to the austereevent data on one or more entities associated with the schedule data. Assuch, the method 40900 may further include at 40935, following theschedule to mitigate the effect of the austere event corresponding tothe austere event data on one or more entities associated with theschedule data.

Referring to FIG. 130 , certain further aspects of the method 40900 aredescribed following, any one or more of which may be present in certainembodiments. The schedule data corresponds to a portion of a schedule41070 that is less than the entire schedule.

In embodiments, another agglomerate model, a detailed shift schedulingsolution, might be focused on the individual hours that staff will work,and, as such, there may be other requirements the model considers, e.g.,having a developer on-call 24/7 to address emergency maintenance orsupport requirements.

Yet another agglomerate model may convert staffing shortfalls intorecruiting requests and onboarding estimates, e.g., when new staff maybe available.

Still yet another agglomerate model may estimate attrition levels basedon past performance, sentiment, schedule-based sentiment impacts (toomany hours, not enough hours, etc.).

Still yet another agglomerate model may provide for scheduleexperimentation, e.g., allowing an AI, ML, and/or a human to experimentand/or explore one or more spaces relating to one or more schedules.Embodiments may provide for the design and/or execution of simulatedexperiments and/or real experiments (conducted in the real world) withAI learning from the results. The selection of executed experiments(and/or implementation of changes based on the results) may be automaticand/or manual. The system may provide for one or more risk tolerancecontrols, e.g., dials and/or sliders, that allow a user and/or AI todefine how much they are willing to risk a poor outcome on anexperiment. Embodiments may also provide for the ability of an employeeto opt-in to an experiment, wherein the employee may be enticed to do sovia pay incentives, e.g., an extra $1.00/hr, and/or other incentives.Embodiments may also provide for an employee to opt-out of anexperiment.

Still yet another agglomerate model may estimate absenteeism. Such amodel may take as inputs, weather models, illness models, holiday and/orevent-based models, pay (absolute), pay (relative to market), pay(bonuses and benefits), promotion opportunities, etc.

As discussed herein with respect to module/component 112 (FIG. 1 ), someembodiments of the current disclosure may provide for a schedule marketand/or marketplace, e.g., a bidding system/process and/or othermarket-based system/process for letting employees compete for shifts.Embodiments of the marketplace may provide for the comparison of “bids”on a schedule and/or time slot. Embodiments of the marketplace may alsoprovide for guards that mitigate and/or prevent undesirable employeeshifts, e.g., all experienced employees on the same shift while othershifts have only inexperienced employees. Such guards may prevent thehighest bid from winning based on one or more considerations, e.g., theperson placing the bid. For example, a highest bid may not win if it isfrom an experienced employee. In other words, embodiments of themarketplace may weigh bids differently based on employer considerations,to include the turnover and/or schedule flexor consideration disclosedherein. Embodiments of the marketplace may also provide for privacypolicies to mitigate sensitive employee bids from being viewed bymultiple employers and/or bosses within a single organization.

Accordingly, referring to FIG. 131 , an apparatus 100100 for lettingemployees/workers 100112, 100113 of a common entity 100104 compete forupcoming shifts 100114 and for obtaining insights regarding shiftproperties 100118 are depicted. The apparatus 100100 may include aschedule display circuit 100102 to display to a customized view 100108of a plurality of upcoming shifts 100114. Workers 100112, 100113 mayeach have one or more worker properties 100302 (as depicted in FIG. 133and described elsewhere herein) that may include worker skills 100304,seniority 100308, a ranking 100310, a qualification 100312, acertification 100314, a classification 100318, and the like as describedelsewhere herein. Upcoming shifts 100114 may include a variety of shiftproperties 100118 (as depicted in FIG. 134 and described elsewhereherein) such as time slot 100402, overtime multiplier 100404, a jobclassification 100408, required certifications 100410, skills 100412, orqualifications required of workers on the shift 100114, and the like asdescribed elsewhere herein. The customization of the customized view100108 may be based, at least in part on an identity of a worker (theidentified worker 100112), to whom the customized view 100108 is beingdisplayed and their corresponding worker properties 100302, as well asthe shift properties 1000118. For example, if an upcoming shift 100114requires that workers 100112, 100113 be certified on a particular pieceof equipment, that upcoming shift 100114 would be displayed only toworkers 100112, 100113 having that required certification. The apparatus100100 may further include a biding interface circuit 100120 to alloweach of the workers 100112, 100113 to submit a bid 100122 on any of theupcoming shifts 100114 shown in their customized view 100108, e.g., eachidentified worker 100112 may bid on any of the upcoming shifts 100114 intheir customized view 100108 (upcoming shifts for which they arequalified, e.g., their corresponding worker properties 100302 and theshift properties 1000118 are compatible).

A bid evaluation circuit 100124 may evaluate each submitted bid 100122and determine a bid quality value 100128 for each bid 100122 based oncorresponding shift properties 100118, the bid 100122, and workerproperties 100302 corresponding to the worker 100113 that submitted thebid 100122. Based on the submitted bids 100122 and bid quality values100128, a winning bid 100130 is determined for a particular upcomingshift 100114. A shift allocation circuit 100132 may then allocate theupcoming shifts 100114 to different workers 100113 based on the winningbids 100130. The allocated shifts 100134 may then be provided to thedifferent workers 100113.

A bid 100120 may be made in terms of bidding tokens 100122. Biddingtokens 100122 may be provided to workers 100112, 100113 based on equalamounts, seniority, as token of appreciation, and the like. A bid 100120may be made in terms of local currency, digital currency (e.g.,BitCoin), and the like. A worker 100112 may submit bids 100122 fordifferent amounts for different upcoming shifts 100114 based on thecorresponding shift properties 100118, their corresponding workerproperties 100302, e.g., skill set, qualifications, and the like. Forexample, a worker 100112 may submit higher bids 100120 for upcomingshifts having higher shift multipliers, better time slots, and the like.A worker 100112 may submit lower bids for upcoming shifts 100114 havingless desirable shift properties 100302 such as an inconvenient time slot(e.g., C shift), date (e.g., a holiday), working conditions/location,and the like.

In embodiments, apparatus 100100 may include a schedule creation circuit100138 to develop a draft schedule 100144, based at least in part, onthe assigned upcoming shifts 100134. A schedule interpretation circuit100142 to interpret schedule data 100150 associated with the proposeddraft schedule 100144 and a warden circuit 100148 (described in detailelsewhere herein) to determine, based at least in part on the scheduledata 100150, that a property of the schedule data 100150 violates aschedule norm 100152. In response to a violation of a schedule norm100152, the shift allocation circuit 100132 may revise one or moreassignments for the upcoming shifts (the assigned shifts 100134).

Referring to FIG. 132 , in embodiments, the apparatus 100100 may includea group detection circuit 100202 to detect a trading group 100204. Thegroup detection circuit 100202 evaluates schedule data 100150, bids100122, workers 100113 and worker properties 100302, schedule data100150, historic schedule data 100210, and the like to determine whetherthere is a trading group 100204, e.g., a subset of workers who appear tohave coordinated their bids 100122, or trades 100208 of assigned shifts100134 amongst the subset of workers 100113. The warden circuit 100148may further evaluate the schedule data 100150, the bids 100122 andtrades 100208 of the trading group 100204 to determine whether theworkers 100113 of the trading group 100204 are benefiting to thedeterminant of workers 100113 that are not part of the trading group100204. The trading group 100204 may benefit by bidding or trading insuch a way that they are assigned more preferred shifts relative toworkers 100113 that are not part of the trading group 100204.

Referring to FIG. 135 , a method 100500 may include displaying acustomized view of a plurality of upcoming shifts 100502, where the viewis customized based on the identity of the worker to whom the view isbeing shown and the customization is based on alignment between a workerproperty corresponding to the identified worker and any required shiftproperties. Different view may be shown to each worker in the entitywhere the different views are each customized according to theindividual worker properties. Based on the upcoming shifts displayed,each worker may enter a bid for one or more of the upcoming shifts wherethe bid is made in terms of bidding tokens, local currency, digitalcurrency (e.g., BitCoin), and the like. Bidding tokens may be providedto workers based on equal amounts, seniority, as token of appreciation,and the like. The bids entered by each worker may be based on a varietyof factors including each worker's like/dislike of various shiftproperties, their availability, external constraints, and availablebidding tokens/currency, and like.

The method 100500 may further include interpreting a plurality of bids100504 and evaluating each submitted bid 100508. The method 100500 mayfurther include determining a bid quality value for each bid 100510 anddetermining the winning bid 100512. The bid quality value may be based,at least in part, on the bid, shift properties, worker propertiescorresponding to the worker who submitted the bid, and the like. The bidquality value may then be used in the determination of the winning bid.The method 100500 may further include assigning upcoming shifts to theworker corresponding to the winning bid for that shift 100514.

In embodiments, the method 100500 may further include determining ashift property quality value 100518 for a subset of the availableupcoming shifts, where the subset of shifts may include two or moreshifts but have at least one shift property in common. The method 100500may further include identifying a bidding trend 100520 associated withthe common shift property. In an illustrative example, bids for a subsetof upcoming shifts may be lower when each of the upcoming shifts has alower than average overtime multiplier compared with a different subsetof similar upcoming shifts with an average or higher than averageovertime multiplier. A bidding trend of bids declining with a decline inovertime multiplier might be identified. The method 100500 may furtherinclude adjusting a shift property for future shifts 100522 based onidentified shift property quality values and/or bidding trends.

Referring to FIG. 136 , instructions 100600 for a processor aredepicted. The instructions may be stored on a non-transitorycomputer-readable medium and are structured to adapt a processor toimplement the instructions. The instructions 100600 may includedisplaying a customized view 100602 of a plurality of upcoming shiftsand interpreting a plurality of bids 100604 for the upcoming shifts. Theview may be customized based on the identity of the worker to whom theview is being shown and the customization is based on alignment betweena worker property corresponding to the identified worker and anyrequired shift properties. Different view may be shown to each worker inthe entity where the different views are each customized according tothe individual worker properties. Based on the upcoming shiftsdisplayed, each worker may enter a bid for one or more of the upcomingshifts where the bid is made in terms of bidding tokens, local currency,digital currency (e.g., BitCoin), and the like. Bidding tokens may beprovided to workers based on equal amounts, seniority, as token ofappreciation, and the like. The bids entered by each worker may be basedon a variety of factors including each worker's like/dislike of variousshift properties, their availability, external constraints, andavailable bidding tokens/currency, and like.

The instructions 100600 may further include evaluating each bid 100608and determining a bid quality value for each bid 100610. The bid qualityvalue may be based, at least in part, on the bid, the shift property,and a worker property of a corresponding worker who submitted the bid.The instructions 100600 may further include determining a winning bid100612 from the submitted bids and assigning upcoming shifts to workers100614 corresponding to the winning bids for each shift.

Referring to FIG. 137 , an apparatus 100700 for enabling workers to bidon upcoming shifts is depicted. The apparatus 100700 may include aschedule display circuit 100702 to display a customized view 100704 of aplurality of upcoming shifts and a bidding interface circuit 100708 toenable a plurality of workers 100712 to submit a bid 100710 for anupcoming shift 100114. The workers 100712 may be employed by a commonentity, a set of related entities, or the like. Each worker 100712 mayhave a set of corresponding worker properties 100302, and each upcomingshift 100114 have a set of corresponding shift properties 100118 asdescribed elsewhere herein. The customized view 100704 may be based, atleast in part on shift properties 100118 and worker properties 100302.For example, schedule display circuit 100702 may customize each viewsuch that each worker 100712 sees only upcoming shifts 100114 for whichtheir worker properties 100302 (e.g., ranking, qualification, skills,experience, etc.) meet the requirements specified in the shiftproperties 100118. In some embodiments, the customized view 100704 mayinclude upcoming shifts 100114 for which the worker 100712 almostqualifies except for lack of experience (as determined based on theworker properties 100302 as well as indications of how the worker 100712might gain the needed experience in order to qualify for these upcomingshifts 100114. In embodiments, a customized view might indicate thatafter a worker 100712 had worked a certain number of shifts in one role,they might qualify to work different roles or shifts in the future. Forexample, after three shifts in an assistant role, a worker might bequalified to work in a primary role on similar shifts.

A bid evaluation circuit 100714 may be structured to evaluate each ofthe bids 100710, determine a corresponding bid quality value 100718, anddetermine a winning bid 100720 based at least in part on the bid qualityvalue 100718. A shift allocation circuit 100722 may assign each upcomingshift (assigned shifts 100724) to one of the plurality of workers 100712corresponding to the winning bid 100720.

The apparatus 100700 may further include a shift evaluation circuit100728 to determine shift property quality values 100730 for each of asubset of the upcoming shifts 100114 based, at least in part, on thebids 100710, the bid quality value 100718, the shift properties 100118,or combinations or statistics based on the foregoing. Each of the subsetshares at least one common shift property 100118. The apparatus 100700may further include a shift creation circuit 100732 to determine shiftproperties 100118 for future shifts based on the shift property qualityvalue 100730. For example, based on a shift property 100118 having a lowshift property quality value 100730, the shift creation circuit 100732may try to pair shifts having a property with a low shift propertyquality value 100730 with a shift property having a high shift propertyquality value 100730 to make the overall shift more desirable. Forexample, C-shifts may have a low shift property quality value and sofuture C-shifts may be designed with a desirable shift property such ashigher overtime multiplier property.

In embodiments, the shift evaluation circuit 100728 may also identify abidding trend 100734 associated with one or more of the shift propertyquality values 100730. A trend provisioning circuit 100738 may transmitthe bidding trend 100734 to a schedule warden circuit 100740. A biddingtrend 100734 may identify a commonly preferred shift or a commonlydisfavored shift. Common shift property quality values 100730 maycorrespond with a bidding trend 100734. The schedule warden circuit100740 may interpret the bidding trend 100734 to verify that the biddingtrend 100734 does not violate a schedule norm 100152.

In embodiments, the apparatus 100700 may further include a data storagecircuit 100742 to store shift bidding data 100744 on a distributedledger 100748. The stored shift bidding data 100744 accumulates ashistoric shift bidding data 100750. The shift bidding data 100744 mayinclude a shift 100114, associated shift properties 1000118, associatedbids 100710, worker properties 100302 corresponding to the worker 100712who made a particular bid 100710, and the like. The historic shiftbidding data 100750 may be used to train a shift evaluation model 100749to be used by the shift evaluation circuit 100728.

Thus, as will be appreciated, the present disclosure describes anapparatus, the apparatus according to one disclosed non-limitingembodiment of the present disclosure may include a schedule displaycircuit to display, to an identified worker of a plurality of workers, acustomized view of a plurality of upcoming shifts, wherein thecustomization of the view is based, at least in part, on the identifiedworker of the plurality of workers, and wherein each of the plurality ofworkers is employed by a same entity and has a corresponding workerproperty, a bidding interface circuit to enable each of the plurality ofworkers to submit a bid for an upcoming shift of the plurality ofupcoming shifts, wherein each shift of the plurality of upcoming shiftshas a shift property, a bid evaluation circuit to evaluate each bidsubmitted by the plurality of workers, determine a bid quality value forthe bid based, at least in part, on the bid, the shift property, and theworker property of a corresponding worker who submitted the bid, anddetermine a winning bid from the submitted bids based at least in parton the bid quality value, and a shift allocation circuit to assign theupcoming shift to the worker of the plurality corresponding to thewinning bid.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include a schedule creation circuit to develop a proposeddraft schedule based, at least in part, on the assigned upcoming shifts,a schedule interpretation circuit to interpret schedule data associatedwith the proposed draft schedule, and a warden circuit to determine,based at least in part on the schedule data, that a property of theschedule data violates a schedule norm, wherein the shift allocationcircuit further revises an assignment of the upcoming shifts of theplurality of upcoming shifts in response to the determination.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include a group detection circuit to detect a tradinggroup, wherein the schedule warden circuit is notified of the tradinggroup and further evaluates the bids and trades of members of thetrading group to determine if the trading group is benefiting to adetriment of employees outside of the trading group.

The present disclosure describes a method, the method according on onedisclosed non-limiting embodiment of the present disclosure may includedisplaying, to an identified worker of a plurality of workers, acustomized view of a plurality of upcoming shifts, wherein thecustomization of the view is based, at least in part, on the identifiedworker of the plurality of workers, and wherein each of the plurality ofworkers is employed by a same entity and has a corresponding workerproperty, interpreting a plurality of bids, each bid submitted by one ofthe plurality of workers for an upcoming shift of the plurality ofupcoming shifts, wherein each shift of the plurality of upcoming shiftshas a shift property, evaluating each bid submitted by the plurality ofworkers, determining, a bid quality value for each bid based, at leastin part, on the bid, the shift property, and the worker property of acorresponding worker who submitted the bid, determining a winning bidfrom the submitted bids based at least in part on the bid quality value,and assigning the upcoming shift to the worker of the pluralitycorresponding to the winning bid.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the view of upcoming shifts isbased, at least in part, on the shift property.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include determining a shift property quality value basedon the bids for a subset of the plurality of upcoming shifts, whereineach of the subset has at least one common shift property.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the subset of shifts includestwo or more shifts.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include identifying a bidding trend associated with theat least one common shift property of the subset of the plurality ofupcoming shifts.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include adjusting at least one shift property for futureshifts based at least in part on the shift property quality value.

The present disclosure describes a non-transitory computer-readablemedium storing instructions, the non-transitory computer-readable mediumstoring instructions according on one disclosed non-limiting embodimentof the present disclosure may adapt at least one processor to display toan identified worker of a plurality of workers, a customized view of aplurality of upcoming shifts, interpret a plurality of bids eachsubmitted by one of the plurality of workers for an upcoming shift ofthe plurality of upcoming shifts, wherein each shift of the plurality ofupcoming shifts has a shift property, evaluate each bid submitted by theplurality of workers, determine a bid quality value for each bid based,at least in part, on the bid, the shift property, and a worker propertyof a corresponding worker who submitted the bid, determine a winning bidfor each shift from among the submitted bids based at least in part onthe bid quality value, and assign the upcoming shift to the worker ofthe plurality corresponding to the winning bid.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the customization of the viewis based, at least in part, on the identified worker of the plurality ofworkers.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein each of the plurality ofworkers is employed by a same entity.

The present disclosure describes an apparatus, the apparatus accordingon one disclosed non-limiting embodiment of the present disclosure mayinclude a schedule display circuit to display a customized view of aplurality of upcoming shifts, a bidding interface circuit to enable eachof a plurality of workers, each employed by a same entity and having acorresponding worker property, to submit a bid for an upcoming shift ofthe plurality of upcoming shifts, wherein the bid includes an offeredamount of a currency, and each shift of the plurality of upcoming shiftshas a shift property, a bid evaluation circuit to: evaluate each bidsubmitted by the plurality of workers, determine a bid quality value foreach bid based, at least in part, on the currency, the shift property,and the worker property of the corresponding worker who submitted thebid, and determine a winning bid from the submitted bids based at leastin part on the bid quality value, and a shift allocation circuit toassign the upcoming shift to the worker of the plurality correspondingto the winning bid.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the customized view ofupcoming shifts is determined based, at least in part, on a workerproperty.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include a shift evaluation circuit to determine a shiftproperty quality value for each of a subset of the plurality of upcomingshifts based, at least in part, on the bids, or the bid quality value,wherein each of the subset shares a common shift property, and a shiftcreation circuit structured to determine shift properties for futureshifts based, at least in part, on the shift property quality value.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the shift evaluation circuitfurther identifies a bidding trend associated with one or more of theshift property quality value, and a further including a trendprovisioning circuit to transmit the bidding trend.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the bidding trend includes acommonly preferred shift.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the bidding trend includes acommonly disfavored shift.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include a schedule warden circuit to interpret thebidding trend and verify that the bidding trend does not violate aschedule norm.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include a data storage circuit to store shift biddingdata on a distributed ledger as historic shift bidding data, wherein theshift bidding data includes shift properties, and corresponding bids,and wherein the shift evaluation circuit includes a model trained on thehistoric shift bidding data.

As described herein, embodiments of the current may include agglomeratemodel connectors, e.g., module/component 130 (FIG. 1 ). In embodiments,the system may create Agglomerate Model Connectors, also referred toherein as simply “connectors”, between agglomerated models/networks thatconsume the outputs of other agglomerated models. For example, in afirst evolution, the agglomerated project scheduler may develop with ananticipated mix of available developers naively estimated based onavailable staffing, no attrition or recruitment, no understanding ofcompeting personnel and resource demands, etc. The model may not haveconsidered whether the appropriate mix of individuals is available, whatother tasks they might be required to perform, or predictively, whencertain staff may be unavailable (on another project, PTO, etc.). Whencreating multiple schedules, a sub-portion of a schedule (ex aparticular set of users or set of days or set of shifts, etc.) may becommon across multiple schedules. In some embodiments, this common areamay be considered higher confidence. In some UIs, this portion of theschedule may be shown in different color/font/etc. As disclosed herein,connectors may transform the outputs of a first agglomerated networkinto a format suitable for input to another agglomerated network.Non-limiting examples of transformations provided by connectors, alsoreferred to herein as connector transformations, include: data formatmanipulations, e.g., short integer format to long integer format,degrees Celsius to degrees Fahrenheit, etc.; data projections, e.g.,projecting data in a first domain onto a second domain; programmableobject conversions, e.g., transferring properties of a firstprogrammable object to another; and/or other types of datatransformations, conversions from a non-normalized format to anormalized format, and/or conversions from a first date/time format to asecond date/time format. Embodiments of the current disclosure may alsoprovide for an agent and/or process that determines whether, and whattype, of connectors are needed to pass data from one agglomeratednetwork to another. Such determinations may be based at least in part ondetecting the expected output format and/or data types of a firstagglomerated network and comparing them to the expected inputs ofanother agglomerated network which is to receive, as input, data fromthe first agglomerated network.

In embodiments, connector biases may be used to adapt outputs of modulesto conditions and/or predictions of an agglomerate network. Connectorsserve as the links/data tunnels that pass data between modules and/ormanipulate inputs and/or outputs of modules, e.g., connectors chainmodules together. In embodiments, connectors may weigh the outputs ofone or more models. Connectors may be modules and may be configured withalgorithms and/or circuits that learn which bias is appropriate to applyin a given situation. Connectors may also connect to other connectors.In embodiments, connectors may have the following relationships betweenmodules and/or connectors on either the input and/or output sides:one-to-one; one-to-many; many-to-one; and/or many-to-many.

In embodiments, the agglomerate network connectors may be configuredwith algorithms and/or circuits to learn over time that outputs ofmodules (such as schedules) may require adjustments for some situations(such as weather events) to generate an acceptable schedule. Inembodiments, agglomerate network connectors may be configured withalgorithms and/or circuits that can be trained to make adjustments todata that passes through the connector. Outputs of modules may be biasedin response to data from other modules using trained connectors. Inembodiments, biasing may include adjusting the values of outputs of amodule. Biasing may include changing a schedule such as movingemployees, blocks, times, and the like. The magnitude of biasing and/orwhat types of biasing is implemented may depend on the level ofuncertainty associated with the outputs of the scheduling module and/orother modules. In one example, higher uncertainty may correspond to ahigher bias of values to increase a safety margin.

Outputs of modules may include an object element that includes data(such as a schedule) and metadata and may be propagated through theagglomerate network. Metadata may include a history of biases applied tothe data, a history of modules/modes used in generating the data, and/orfeedback that was applied to the data.

In embodiments, module biases, module results,confidences/uncertainties, module and/or connector histories, and/orrelated metadata may be passed between, and/or otherwise accessed, bymodules and connectors via the matrix of biases, as disclosed in FIG.152 , FIG. 153 , and the related description.

As disclosed in greater detail herein, embodiments of the currentdisclosure provide for using biases to adapt outputs of modules toconditions and/or predictions of an agglomerate network. Referring toFIG. 138 , an apparatus 190100 includes an agglomerate network circuit190102 structured to interpret input data 190104 and transmit outputdata 190108. The apparatus 190100 may further include a connectorcircuit 190110 structured to bias at least one of the input data 190104prior to interpretation by the agglomerate network circuit 190102 or theoutput data 190108 prior to transmission by the agglomerate networkcircuit 190102. Referring to FIG. 139 , the apparatus 190100 may includea plurality of connector circuits 190110. In some embodiments, each ofthe connector circuits 190110 may have an acceptable range for a biasadjustment 190210. A connector feedback loop 190212 may continue untilthe biases of all connector circuits 190110 are within theircorresponding acceptable range. In some embodiments, once a threshold isreached, the connector feedback loop 190212 may terminate.

In one aspect, the output data 190108 of an agglomerate network may beschedule data 190202, and the schedule data 190202 may have a scheduleproperty 190204. For example, the schedule property 190204 may be ashift, such as work shift duration, start time, or end time, or may be apersonnel selection.

Referring to FIG. 140 , the apparatus 190300 may include a plurality ofagglomerate network circuits 190302 and a plurality of connectorcircuits 190310. Each of the plurality of agglomerate network circuits190302 may be structured to interpret input data 190304 and transmitoutput data 190308. Each of the plurality of connector circuits 190310may be structured to interpret the output data 190308 of a firstcorresponding agglomerate network circuit 190302 of the plurality, biasthe interpreted output data 190314, and transmit the biased interpretedoutput data 190318 as the input data 190304 of a second correspondingagglomerate network circuit 190302 of the plurality. Referring to FIG.141 , each of the connector circuits 190310 may have an acceptable rangefor a bias adjustment 190410 and a connector feedback loop 190412 maycontinue until the biases of all connector circuits 190310 are withintheir corresponding acceptable range. In an aspect, the output data190308 of an agglomerate network may be schedule data 190402 and mayhave a schedule property 190404. For example, the schedule property190404 may be at least one of a shift or a personnel selection.

Referring to FIG. 142 , a procedure 190500 may include an operation190502 of interpreting, via an agglomerate network circuit, input data.The procedure 190500 may also include an operation 190504 oftransmitting, via the agglomerate network circuit, output data. Theprocedure 190500 may include a further operation 190508 of biasing, viaa connector circuit, at least one of the input data prior tointerpretation via the agglomerate network circuit or the output dataprior to transmission via the agglomerate network circuit.

Referring now to FIG. 143 , a procedure 190600 may include an operation190602 of interpreting, via a first agglomerate network circuit, firstinput data; an operation 190604 of generating, via the first agglomeratenetwork circuit, first output data based at least in part on the firstinput data; an operation 190608 of biasing, via a first connectorcircuit, the first output data; an operation 190610 of interpreting, viaa second agglomerate network circuit, the biased first output data assecond input data; an operation 190612 of generating, via the secondagglomerate network circuit, second output data based at least in parton the second input data; an operation 190614 of biasing, via a secondconnector circuit, the second output data; an operation 190618 ofinterpreting, via a third agglomerate network circuit, the biased secondoutput data as third input data; an operation 190620 of generating, viathe third agglomerate network circuit, third output data based at leastin part on the third input data; and an operation 190622 of transmittingthe third output data.

In embodiments of either procedure 190500 or procedure 190600, biasingvia the connector circuit may include at least one of increasing ordecreasing at least one of the input data or the output data. In anotherembodiment, biasing via the connector circuit may include adjusting theinput data or the output data based at least in part on connectorcircuit input data. The connector circuit input data may be generated byan agglomerate network circuit distinct from the one that generated theoutput data or the input data being biased by the connector circuit. Theconnector circuit input data may be generated by another connectorcircuit. In an embodiment, biasing via the connector circuit may includeweighting at least one of the input data or the output data. In anembodiment, biasing via the connector circuit may be based at least inpart on a confidence value associated with the input data or the outputdata being biased. A different biasing approach may be used depending onthe confidence value. In an embodiment, the biases may be stored in amatrix or other applicable data structure. The confidence values mayalso be stored in the matrix. In an embodiment, biasing via theconnector circuit may be based at least in part on a value determinedvia machine learning. In an embodiment, the output data of anagglomerate network may be schedule data having a schedule property(e.g., date, time, week, duration, blocked days), wherein biasing theoutput data via the connector circuit may include shifting the scheduleproperty. The schedule property may be a shift and shifting the scheduleproperty may include adjusting the shift. For example, adjusting theshift may include moving the shift or changing a length of the shift.The schedule property may be a personnel selection. For example, biasingthe output data via the connector circuit may include adjusting thepersonnel selection.

In an example of connector biasing, a schedule and plan may becalculated for delivery of oversize equipment to a site. Many factorsmay go into preparing the schedule and plan, and the schedule and planmay require input from multiple parties (e.g., town officials, highwaysafety officers, utility companies, human resources, weather data,traffic data, etc.). Biasing of input data may occur at the party levelwhere one party's input is more heavily weighted, or may occur moregranularly, such as weighting specific types of data from individualparties. Continuing with the example, in one instance, biasing may favorinput from the highway safety officers and the weather data. In anotherinstance, biasing may favor the availability of truckers in order tocomply with mandatory rest periods.

In embodiments, a non-transitory computer-readable medium may storeinstructions that adapt at least one processor to interpret, via a firstagglomerate network circuit, first input data; generate, via the firstagglomerate network circuit, first output data based at least in part onthe first input data; bias, via a first connector circuit, the firstoutput data; interpret, via a second agglomerate network circuit, thebiased first output data as second input data; generate, via the secondagglomerate network circuit, second output data based at least in parton the second input data; bias, via a second connector circuit, thesecond output data; interpret, via a third agglomerate network circuit,the biased second output data as third input data; generate, via thethird agglomerate network circuit, third output data based at least inpart on the third input data; and transmit the third output data.Biasing via the connector circuit may include at least one of increasingor decreasing at least one of the input data or the output data,adjusting the input data or the output data based at least in part onconnector circuit input data, or weighting at least one of the inputdata or the output data. Biasing via the connector circuit may be basedat least in part on a confidence value associated with the input data orthe output data being biased or on a value determined via machinelearning.

In embodiments, a connection module may be trained to determine and/orapply a bias. A connection module may be trained using training datausing one or more supervised or unsupervised training methods. In oneembodiment, a set of training data may include labeled data relating toa state of an agglomerate network and data that includes adjustmentsmade to data by a user (manual biases made by a user). A machinelearning model (such as a neural network) may be trained on the data topredict when a bias is applied to the data. In one example, a neuralnetwork may be a classifier that takes as input the state data of anagglomerate network (data including one or more data inputs, metadata,configuration data, etc.), and generates an output the identifies if anoutput should be biases and/or the magnitude of the bias and/or a typeof bias to be applied. Training data may be used to train the network(such as using back propagation) using the labeled data. After training,the trained model may be used to automatically identify if biases shouldbe applied, the magnitude of bias, and/or the type of bias that shouldbe applied. In embodiments, the trained modules may be refined based onchanges from users (such as manual bias overrides, feedback from usersregarding quality of schedules produced, etc.).

Disclosed herein are details of how biases, e.g., module/component 132(FIG. 1 ), are applied to different modules and different types of data.In some embodiments, biasing may include direct manipulation of anoutput of a module (such as increasing or decreasing a value, changing aschedule, and the like). In some embodiments, outputs of modules may notbe directly adjusted (such as a complex schedule where modification ofone aspect of a schedule may affect other constraints). In embodiments,biasing may include adjustments of inputs to an upstream module. Biasingof a scheduler module may include biasing the inputs (such asconstraints) of the scheduler module. Connectors may also use machinelearning to learn how to bias module inputs and/or outputs.

In embodiments, a connector may be trained to determine how to modifyinputs of a module (such as a scheduler module) to obtain the desiredbias at the output of the module. The biasing of inputs may be aniterative process where small changes are made to the inputs and thechanges at the outputs are observed, with additional changes made to theinputs until the desired bias at the output is reached. The biasingmodule may be self-adjusting to identify and learn appropriate changesthat may be made to the inputs. In some cases, the biasing of the inputsmay be limited to a predefined subset of inputs. The biasing circuit mayhave a termination and/or predefined biasing limit wherein a bias beyonda certain level causes the connector to terminate biasing and/or discarda schedule. In embodiments, a connector may receive the results ofseveral competing modules and select one or more “winners” to bias andpass their results on.

In embodiments, connectors may determine how many schedules should bedeveloped at a given point within the agglomerate network, and inparticular, in scenarios where schedules are noncontinuous.

As disclosed in greater detail herein, embodiments of the currentdisclosure provide for applying biases to different modules anddifferent types of data. For example, only certain biases may be appliedto certain data based on the data categorization, or only certainbiasing methods may be employed. In another example, input data may bemodified iteratively to obtain desired biasing parameters for outputdata, which may serve to train biasing circuits.

In one aspect, referring to FIG. 144 , an apparatus 200100 may includean agglomerate network circuit 200102 structured to interpret input data200104 and transmit output data 200108. The apparatus 200100 may alsoinclude a connector circuit 200110 structured to: receive biasingparameters 200112 for the input data, categorize the input data 200104based on types of biases 200114 that can be applied to the input data200104, map biasing parameters 200112 to the categorized input data, andbias the input data based on the mapping. The connector circuit 200110may be further structured to determine which data can be directlybiased. The connector circuit 200110 may be further structured todetermine numerical data that can be directly biased or schedule datathat can be indirectly biased.

Referring now to FIG. 145 , input data 200104 may be schedule datagenerated by a schedule module 200202. The connector circuit 200110 maybe further structured to determine biasing methods for each category,such as direct biasing methods including at least one of applying apercentage change, modifying a value based on a function, or applying atable look up, or indirect biasing methods including at least one ofselecting an alternate module for generating input data, or modifyingparameters of a module generating the input data.

Referring now to FIG. 146 , an apparatus 200300 may include anagglomerate network circuit 200302 structured to receive input data200304. The apparatus 200300 may further include a bias interpretationcircuit 200310 structured to receive biasing parameters 200312 for theinput data 200304, a categorizing circuit 200318 structured tocategorize the input data 200304 based on types of biases 200314 thatcan be applied, a mapping circuit 200320 structured to map the biasingparameters 200312 to the categorized input data, and a biasing circuit200322 structured to bias the input data 200304 based on the mapping.The agglomerate network circuit 200302 may be further structured totransmit the biased input data, which may be as output data 200308. Thebias interpretation circuit 200310 may be further structured todetermine at least one of which data can be directly biased, numericaldata that can be directly biased, or schedule data that can beindirectly biased.

In embodiments, and referring to FIG. 147 , input data may be scheduledata generated by a schedule module 200402. In embodiments, the biasinterpretation circuit 200310 may be further structured to determinebiasing methods for each category. Direct biasing methods may include atleast one of applying a percentage change, modifying a value based on afunction, or applying a table look up. Indirect biasing methods mayinclude at least one of selecting an alternate module for generatinginput data, and/or modifying parameters of a module generating the inputdata.

Referring to FIG. 148 , an apparatus 200500 may include an agglomeratenetwork circuit 200502 structured to receive input data 200504, a biasinterpretation circuit 2005010 structured to receive biasing parameters200512 for the input data 200504, a mapping circuit 200520 structured tomap the biasing parameters 200512 to the input data 200504, a biasingmethodology circuit 200518 structured to determine a biasing method200524 based on the mapping, and a biasing circuit 200522 structured tobias, using the determined biasing method, the input data based on themapping. The agglomerate network circuit 200502 may be furtherstructured to transmit the biased input data. The bias interpretationcircuit 200510 may be further structured to determine at least one ofwhich data can be directly biased, numerical data that can be directlybiased, or schedule data that can be indirectly biased. Input data200504 may be schedule data generated by a schedule module. The biasinterpretation circuit 200510 may be further structured to determinebiasing methods for each category. Direct biasing methods may include atleast one of applying a percentage change, modifying a value based on afunction, or applying a table look up. Indirect biasing methods mayinclude at least one of selecting an alternate module for generatinginput data, and/or modifying parameters of a module generating the inputdata.

Referring to FIG. 149 , an apparatus 200600 may include an agglomeratenetwork circuit 200602 structured to receive input data 200604, performmanipulations on the data and generate output data 200608, a biasinterpretation circuit 200610 structured to receive biasing parameters200612 for the output data 200608, a mapping circuit 200620 structuredto modify input data 200604 and identify correlations between inputmodifications and changes to the output data 200608, and a biasingcircuit 200622 structured to iteratively trigger different input datamodifications at the mapping circuit 200620 and identify input datamodifications that result in desired biasing parameters 200612 for theoutput data 200608. The bias interpretation circuit 200610 may befurther structured to determine at least one of which data can bedirectly biased, numerical data that can be directly biased, or scheduledata that can be indirectly biased. Input data 200604 may be scheduledata generated by a schedule module. The bias interpretation circuit200610 may be further structured to determine a biasing method. Directbiasing methods may include at least one of applying a percentagechange, modifying a value based on a function, or applying a table lookup. Indirect biasing methods may include at least one of selecting analternate module for generating input data, and/or modifying parametersof a module generating the input data.

Referring now to FIG. 150 , a procedure 200700 may include operationsincluding an operation 200702 of receiving, via an agglomerate networkcircuit, input data, an operation 200704 of receiving biasing parametersfor the input data, an operation 200708 of categorizing the input databased on types of biases that can be applied, an operation 200710 ofmapping biasing parameters to the categorized input data, an operation200712 of biasing, at least one of the input data based on the mapping,and an operation 200714 of transmitting, via the agglomerate networkcircuit, the biased input data. Categorizing of input data may includedetermining which data can be directly biased. Categorizing may includeat least one of determining numerical data that can be directly biasedor schedule data that is indirectly biased. Indirectly biasing mayinclude generating feedback to a module generating the input data,wherein the feedback triggers generation of new input data based on thefeedback. Directly biasing may include directly changing a value of theinput. Input data may be schedule data generated by a schedule moduleand biasing includes adjusting the inputs to the schedule module.Mapping biasing parameters may include determining biasing methods foreach category. Direct biasing methods may include at least one ofapplying a percentage change, modifying a value based on a function, orapplying a table look up. Indirect biasing methods may include at leastone of selecting an alternate module for generating input data ormodifying parameters of module generating the input data.

Referring to FIG. 151 , a procedure 200800 may include an operation200802 of receiving, via an agglomerate network circuit, input data, anoperation 200804 of receiving biasing parameters for the input data, anoperation 200808 of mapping biasing parameters to the input data, anoperation 200810 of determining biasing methods based on the mapping, anoperation 200812 of biasing, using the determined biasing methods, theinput data based on the mapping, and an operation 200814 oftransmitting, via the agglomerate network circuit, the biased inputdata. Input data may be schedule data generated by a schedule module andbiasing includes adjusting the inputs to the schedule module. Directbiasing methods may include at least one of applying a percentagechange, modifying a value based on a function, or applying a table lookup. Indirect biasing methods may include at least one of selecting analternate module for generating input data or modifying parameters ofmodule generating the input data.

In embodiments, a non-transitory computer-readable medium may storeinstructions that adapt at least one processor to receive input data,receive biasing parameters for the input data, categorize the input databased on types of biases that can be applied, map the biasing parametersto the categorized input data, bias the input data based on the mapping,and transmit the biased input data.

In embodiments, a non-transitory computer-readable medium may storeinstructions that adapt at least one processor to receive input data,receive biasing parameters for the input data, map the biasingparameters to the input data, determine a biasing method based on themapping, bias, using the determined biasing method, the input data basedon the mapping; and transmit the biased input data.

In embodiments, a connection module may be trained to determine the typeof bias and/or apply a bias. In embodiments, one trained model may beconfigured to determine the type of bias and apply a bias to the data.In some embodiments, a plurality of trained models may be used such asone or more modules may be trained to determine the type of bias (suchas determining if a bias can be directly applied to data or if biasingrequires manipulation of input data to another module). Other modulesmay be trained to apply the bias to the data (such as by determining themagnitude and/or how to manipulate input data of other modules to obtainthe desired output bias). The connection modules may be trained usingtraining data using one or more supervised or unsupervised trainingmethods. In one embodiment, a set of training data may include labeleddata relating to a state of an agglomerate network and data thatincludes adjustments made to data by a user (manual biases made by auser). A machine learning model (such as a neural network) may betrained on the data to predict the type of bias that can be applied andmagnitude of changes required to achieve the bias. In one example, aneural network may be a classifier that takes as input the state data ofan agglomerate network (data including one or more data inputs,metadata, configuration data, etc.), and generates an output theidentifies if the type of bias that can be applied. After training, thetrained model may be used to automatically identify the type of biasesthat can be applied and/or the magnitude of bias. In embodiments, thetrained modules may be refined and/or retrained based on changes fromusers (such as manual bias overrides, feedback from users regardingquality of schedules produced, etc.).

Disclosed herein are details of management and monitoring of biases inthe agglomerate network, e.g., module/component 132 (FIG. 1 ), when aplurality of biases is applied. In some embodiments, each connector maycause/generate a bias independently of other biases or connectors. Insome embodiments, connectors and biases may include a dependency. Insome cases, one connector may cause a bias in one direction whileanother connector may cause a bias in another direction. In some cases,one connector may cause a bias in one aspect and may trigger a bias inanother aspect.

In embodiments, a matrix of biases may be used to store and/or arrangebiases so as to reflect, in part, dependencies of the biases and/or theconnectors that generated them. In addition to recording dependencies,embodiments of the matrix may also be used to define the dependencies soas to specify which biases and/or other connector outputs are fed intowhich connectors and/or modules. In embodiments, the matrix maydetermine how to “net out” the inputs and/or outputs of connectorsand/or modules.

In embodiments, a history of biases (such as the metadata described inFIG. 138 through FIG. 1906 ) may be propagated with the data. Themagnitude, causes, and assumptions that triggered a bias may be trackedand propagated with the data. In embodiments, the data may include databefore a bias was applied such that if opposite biases are specified bydifferent connectors, the data can be returned to the original values.In embodiments, connectors may include rules and thresholds for maximumoverall accumulated biases, number of biasing operations, and the like.Connectors may monitor threshold data of biases and discard a schedulewhen the threshold is exceeded and start with a different schedule.

As disclosed in greater detail herein, embodiments of the currentdisclosure provide for dependencies of connector biases. Referring toFIG. 152 , an agglomerate network 210100 for generating schedule datamay include a scheduler circuit 210102 structured to output a firstschedule data 210108, a first module 210110 structured to apply a firstbias 210114 to the first schedule data 210108 and generate secondschedule data 210118, a second module 210120 structured to receive thesecond schedule data 210118 from the first module 210110 and structuredto manipulate the second schedule data 210118 and generate thirdschedule data 210122, where the manipulation includes applying a secondbias 210104 to the third schedule data 210122, a bias monitoring module210124 structured to monitor the first bias 210114 and the second bias210104 and identify conflicting elements of the first and second biases,and a connector circuit 210128 structured to adjust, responsive toidentifying conflicting bias parameters of at least one of the schedulercircuit 210102, the first module 210110, or the second module 210120 toresolve the conflicting biases. At least one of the first bias 210114 orthe second bias 210104 may be a direct bias or an indirect bias. Thefirst bias 210114 and the second bias 210104 may be associated with apriority. In embodiments, at least one of the first module 210110 or thesecond module 210120 may be further structured to compute an uncertaintyassociated with the bias. In embodiments, conflicting biases may includebiases applied to data that have opposite magnitudes. In embodiments,conflicting biases may be determined by comparing the first scheduledata 210108, the second schedule data 210118, and the third scheduledata 210122. In embodiments, conflicting biases are identified bycomparing similarities of the first schedule data 210108 and the secondschedule data 210118 and differences between second schedule data 210118and third schedule data 210122.

Referring to FIG. 153 , an apparatus 210200 may include a first module210210 configured to apply a first bias 210214 to first data 210208 togenerate second data 210218, a second module 210220 configured toreceive the second data 210218 and configured to apply a second bias210204 to the second data 210218, a first bias monitoring module 210224configured to calculate a combined bias score 210222 based at least inpart on the first bias 210214 and the second bias 210204, a biasadjustment circuit 210228 configured to determine that the combined biasscore 210222 is above a threshold value 210230 and adjust the secondbias 210204, and a bias notification circuit 210232 structured totransmit an indication that the combined bias score 210222 wasdetermined to be above the threshold value 210230. In embodiments, atleast one of the first bias 210214 or the second bias 210204 is a directbias or an indirect bias. In embodiments, the first bias 210214 and thesecond bias 210204 may be associated with a priority. In embodiments, atleast one of the first module 210210 or the second module 210220 arefurther structured to compute an uncertainty associated with the bias.In embodiments, the combined bias is a function of the magnitude of thefirst bias and the magnitude of the second bias. In embodiments, andreferring to FIG. 154 , in response to the transmitting an indication,the first module 210210 is replaced with a third module 210302.

Referring to FIG. 155 , a procedure 210400 may include an operation210402 for propagating data from a first module to a second module, anoperation 210404 for propagating an indication of a first bias appliedto the data from the first module to the second module, an operation210408 for applying a second bias at the second module, an operation210410 for computing a combined bias score based on the first bias andthe second bias, an operation 210412 for determining that the combinedbias score is above a bias threshold value, and an operation 210414 fortransmitting an indication to the first module of the bias thresholdvalue. In embodiments, at least one of the first bias or the second biasmay be a direct bias or an indirect bias. In embodiments, the combinedbias may be a function of the magnitude of the first bias and themagnitude of the second bias. In embodiments, in response to thetransmitting an indication, the first module may be replaced with athird module. In embodiments, the first bias and the second bias may beassociated with a priority. In an embodiment, and referring to FIG. 156, a procedure 210500 may further include an operation 210502 ofadjusting the first bias or the second bias based at least in part onthe priority. In embodiments, applying a bias may include computing anuncertainty associated with the bias. The procedure may further includeadjusting the first bias or the second bias based at least in part onthe uncertainty. In embodiments, the uncertainty may be at least one ofa function of the magnitude of the bias or a function of the type ofdata that is biased. In an embodiment, and referring to FIG. 157 , aprocedure 210600 may further include an operation 210602 of tracking ahistory of biases and triggers for generating the biases. In anembodiment, and referring to FIG. 158 , a procedure 210700 may furtherinclude an operation 210702 of monitoring a number of biasing operationsand generating an indication when the number of biases is above athreshold value.

Referring now to FIG. 159 , a procedure 210800 may include an operation210802 for propagating data from a first module to a second module, anoperation 210804 for applying a first bias to the data at the firstmodule, an operation 210808 for applying a second bias to the data atthe second module, an operation 210810 for monitoring a combined biasthat is based at least in part on the first bias and the second bias, anoperation 210812 for determining that the combined bias is less than thefirst bias or the second bias, and an operation 210814 for adjusting atleast one of the first bias or the second bias to reduce a magnitude ofthe combined bias. In embodiments, at least one of the first bias or thesecond bias may be a direct bias or an indirect bias. In embodiments,the combined bias may be a function of the magnitude of the first biasand the magnitude of the second bias. In embodiments, in response to thetransmitting an indication, the first module is replaced with a thirdmodule. In embodiments, the first bias and the second bias may beassociated with a priority. The procedure 210800 may further includeadjusting the first bias or the second bias based at least in part onthe priority. In embodiments, applying a bias includes computing anuncertainty associated with the bias. The procedure 210800 may furtherinclude adjusting the first bias or the second bias based at least inpart on the uncertainty. In embodiments, the uncertainty may be at leastone of a function of the magnitude of the bias or a function of the typeof data that is biased. In embodiments, and referring to FIG. 160 , aprocedure 210900 may further include an operation 210902 for tracking ahistory of biases and triggers for generating the biases. Inembodiments, and referring to FIG. 161 , a procedure 211000 may furtherinclude an operation 211002 for monitoring a number of biasingoperations and generating an indication when the number of biases may beabove a threshold value.

Embodiments of the current disclosure may also provide for schedulespreading, e.g., the spreading of beneficial changes in a generatedschedule across disjointed organizations, which may be performed viamodules/components 126 and/or 128 (FIG. 1 ). For example, embodimentsmay recommend (or make) a schedule adjustment for Company A where theadjustment proved successful for (or is based on data from) Company B.Changes and/or adjustments to a schedule in one department of a businessmay be applicable to another department within an organization. Changesand/or adjustments to schedules in one organization may be applicable toschedules of another organization, which may be similar or distinct.Embodiments described herein are directed towards applying or spreadingbeneficial changes in a schedule across disjointed entities, such asorganizations, which may include different companies, divisions and/ordepartments within the same organization or across distinctorganizations. In embodiments, an adjustment to a schedule may be madeand/or recommended for Company A, whereas such a schedule adjustmentproved successful for company B. In other embodiments, schedulingadjustments that were found unsuccessful or unproductive for Company Bmay be flagged and discouraged from being applied or recommended toschedules in Company A. Embodiments may detect that a particular companyis experiencing (or about to experience) a situation similar to a pastscenario experienced by the company or one or more other companies.Similar modifications and adjustments may be recommended and/or appliedbased on the previous scenarios that were subsequently determined to bebeneficial. In other embodiments, similar organizations are identifiedfrom organization data. Such similarities are leveraged to determinewhat, if any, past modifications were beneficial. In embodiments, pastmodifications in similar organizations may be given more focus (e.g.,higher weight) when recommending or making schedule adjustments toanother similar organization.

In embodiments, a schedule spreader may be a module that receivesinputs, e.g., a schedule in the form of schedule data and/or other datasuch as biases. A schedule spreader may be configured as a standalonemodule. For example, the schedule spreader may be a module within anagglomerate network that either generates a schedule and incorporatesknown beneficial adjustments or receives an existing schedule and thenedits the schedule to include the beneficial changes. The schedule(e.g., schedule data) can be received directly as input to theagglomerate network or from a schedule generation module within thenetwork. The output of the schedule spreader (a schedule) may be passedto other modules in the agglomerate network for evaluation where theother modules generate output(s), e.g., a bias. The other modules may,in turn, feed the output back into the schedule spreader module to forma feedback loop which tries to reach equilibrium and/or optimization ofvarious biases in the agglomerate network. The connections between theschedule spreader module and the various other modules of theagglomerate network may be accomplished via connectors.

Referring now to FIG. 162 , an apparatus 170100 for schedule spreadingis shown. The apparatus 170100 includes a schedule interpretationcircuit 170102, a schedule adjustment circuit 170104, a spread commandcircuit 170106, and a spread command provisioning circuit 170108. Theschedule interpretation circuit 170102 may be structured to interpretschedule data 170110. The schedule adjustment circuit 170104 may bestructured to maintain a list of recommended schedule adjustments170112. Each recommended schedule adjustment may correspond to one of aplurality of schedule parameters. Non-limiting examples of scheduleparameters include: number of shifts, duration of shifts, number ofassigned workers to a shift, number of available workers for a schedule,wages per shift, amount of over time, estimated sales, estimatedprofits, and/or any other data related to a schedule. The scheduleparameters of the list of recommended schedule adjustments 170112 arereferred to herein as the plurality of first schedule parameters 170114.The schedule adjustment circuit 170104 analyzes the schedule data 170110and identifies a schedule parameter, referred to as a second scheduleparameter 170116. Further, the schedule adjustment circuit 170104identifies a recommended schedule adjustment from the list ofrecommended schedule adjustments 170112. The identification may beperformed by matching the second schedule parameter 170116 to one of theplurality of the first schedule parameters 170114. The one of theplurality of first schedule parameters 170114 may correspond to therecommended schedule adjustment. The spread command circuit 170106 maybe structured to generate a spread command value 170118 structured totrigger a change in the schedule data 170110. The spread command circuit170106 operates in response to the identified recommended scheduleadjustment performed by the schedule adjustment circuit 170104. Thechange in the schedule data 170110 includes adjusting the schedule data170110 according to the recommended schedule adjustment. The spreadcommand provisioning circuit 170108 may be structured to transmit 170120the spread command value 170118, which may be used for performing theadjustment to the schedule data 170110, sent to other modules within anagglomerate network, or stored in a database.

Referring to FIG. 163 , certain further aspects of the apparatus 170100are described following, any one or more of which may be present incertain embodiments. The schedule adjustment circuit 170104 (FIG. 162 )may be further structured for building 170202 (FIG. 163 ) the list ofrecommended schedule adjustments 170112 (FIG. 163 ). The building 170202may be based on user feedback 170204 regarding past adjustments to theschedule data 170110 and/or based on trends 170206 determined from aschedule marketplace, as disclosed herein. In example embodiments, thelist of recommended schedule adjustments 170112 includes scheduleadjustments that successfully improved prior schedules possessing aplurality of third schedule parameters 170208 corresponding to theplurality of first schedule parameters 170114, wherein the improvementis with respect to the second schedule parameter 170116. The pluralityof third schedule parameters 170208 may be used in the future to improvescheduling within a department or an organization or may also be usedfor other entities or departments. In example embodiments, a firstorganization having a plurality of third schedule parameters 170208 maybe similar to a second organization. These third schedule parameters170208 may be used for the second organization when matching the secondschedule parameter 170116. Using these third schedule parameters 170208may lead to improved scheduling for the second organization.

The list of recommended schedule adjustments 170112 may have scheduleadjustments collected via different sources. For instance, exampleembodiments may have the schedule adjustment including prior scheduleadjustments across multiple entities. There may be several types ofentities 170210. In example embodiments, the multiple entities includedistinct departments within the same organization. In another exampleembodiment, the multiple entities include similar departments withindistinct organizations. In another example embodiment, the multipleentities include distinct departments within a distinct organization.Further, the plurality of first schedule parameters 170114 of the listof recommended schedule adjustments 170112 may be indicative of aspecific business scenario 170212. The specific business scenario 170212may be at least one of: risk of employee turnover, decrease inproduction, loss of profits, or employee dissatisfaction. Users of theapparatus 170100 may choose one or more business scenarios for matchinga second scheduled parameter 170116 to one of the plurality of firstschedule parameters 170114.

Further, in embodiments, the schedule adjustment circuit 170104 (FIG.162 ) may be structured based at least in part on an artificialintelligence 170214. In embodiments, the artificial intelligence 170214may be based at least in part on machine learning 170216. Machinelearning 170216 may be that of supervisor machine learning methods,unsupervised machine learning methods, semi-supervised machine learningmethods, or a mix of thereof. In other example embodiments, theartificial intelligence 170214 is based at least in part on a neuralnetwork 170218. In general, methods of machine learning 170216 may betrained on a training data set based at least in part on successfuladjustments to prior schedules (e.g., adjustment to schedule data170110). In other example embodiments, the neural network 170218 may betrained on a training data set based at least in part on successfuladjustments to prior schedules (e.g., adjustment to schedule data170110). As explained above, the adjustment to prior schedules may bebased on schedules from across multiple entities.

Further, in example embodiments, the schedule adjustment circuit 170104(FIG. 162 ) intelligently identifies the second schedule parameter170116. The schedule adjustment circuit 170104 intelligently assigns theidentified schedule adjustment based at least in part on determiningthat a particular business scenario exists based at least in part on theschedule data 170110. In other words, matching the second scheduleparameter 170116 to one of the plurality of first schedule parameters170114 is performed intelligently. One of the intelligent methods mayinclude using artificial intelligence or machine learning methods toidentify the matching. In example embodiments, intelligence may includefocusing on one or more business scenarios such as the risk of employeeturnover, decrease in production, loss of profits, employeedissatisfaction, and the like. When a user selects a specific businessscenario, more focus may be given to the plurality of first scheduleparameters 170114 associated with the business scenario to be matchedwith the second schedule parameter 170116.

In embodiments, the adjustments to schedule data 170110 include severaloperations 170222. The operations 170222 performed to shifts of aschedule may include one or more of moving a shift, adding a shift,deleting a shift, and dividing a shift into two or more new shifts.Further, the operations 170222 performed to personnel/employees in aschedule may also include one or more of moving an employee to anothershift, adding an employee to a shift, or deleting an assigned employeefrom a shift. Adjustments to schedule data 170110 may also includeadjusting a bias of a connector 170220 in an agglomerate network.Therefore, operations performed herein may influence the operations ofthe agglomerate network at large.

FIG. 164 depicts aspects of a method 170300 for schedule spreading. Themethod 170300 may be implemented via the apparatus 170100 or any othercomputing device disclosed herein. The method 170300 includesinterpreting schedule data 170302 and maintaining a list of recommendedschedule adjustments 170304. Each recommended schedule adjustmentcorresponds to one of a plurality of first schedule parameters 170114.The method 170300 includes analyzing the schedule data to identify asecond schedule parameter 170306, and identifying a recommended scheduleadjustment from the list via matching the second schedule parameter toone of the plurality of first schedule parameters corresponding to therecommended schedule adjustment 170308. The method 170300 furtherincludes generating, responsive to the identified recommended scheduleadjustment, a spread command value structured to trigger a change to theschedule data 170310. In embodiments, the change includes adjusting theschedule data according to the recommended schedule adjustment. Themethod 170300 further includes transmitting the spread command value170312.

Turning to FIG. 165 , certain further aspects of the method 170300 aredescribed, any one or more of which may be present in certainembodiments. The method 170300 builds 170402 the list of recommendedschedule adjustments 170404. The building 170402 may be based on userfeedback 170406 regarding past adjustments to the schedule data 170408and/or based on trends determined from a schedule marketplace 170410. Inembodiments, the list of recommended schedule adjustments 170404includes schedule adjustments that successfully improved prior schedulespossessing a plurality of third schedule parameters 170412 correspondingto the plurality of first schedule parameters 170114 (FIG. 162 ),wherein the improvement is with respect to the second schedule parameter170414. The plurality of third schedule parameters 170412 may be used infuture iterations of the method 170300 (and/or other processes disclosedherein) to improve scheduling or may also be used for other entities ordepartments. In example embodiments, a first organization having theplurality of third schedule parameters 170412 may be similar to a secondorganization. These third schedule parameters 170412 may be used for thesecond organization when matching the second schedule parameter 170414.Embodiments may use these third schedule parameters 170412 to improvescheduling for the second organization.

The list of recommended schedule adjustments 170404 may have scheduleadjustments collected via different sources. For instance, exampleembodiments may have the schedule adjustment including prior scheduleadjustments across multiple entities. There may be several types ofentities 170416. In example embodiments, the multiple entities includedistinct departments within the same organization. In another exampleembodiment, the multiple entities include similar departments withindistinct organizations. In another example embodiment, the multipleentities include distinct departments within a distinct organization.Further, the plurality of first schedule parameters 170418 of the listof recommended schedule adjustments 170404 may be indicative of aspecific business scenario 170420. The specific business scenario 170420may be at least one of: risk of employee turnover, decrease inproduction, loss of profits, or employee dissatisfaction. The method170300 may also allow users to choose one or more business scenarios formatching a second scheduled parameter 170414 to one of the plurality offirst schedule parameters 170418.

Further, in embodiments, the method 170300 performs based at least inpart on artificial intelligence 170422. In other example embodiments,artificial intelligence is based at least in part on machine learning170424. Machine learning 170424 may be that of supervised machinelearning methods, unsupervised machine learning methods, semi-supervisedmachine learning methods, or a mix of thereof. In other exampleembodiments, artificial intelligence 170422 may be based at least inpart on a neural network 170426. In general, the methods of machinelearning 170424 may be trained on a training data set based at least inpart on successful adjustments to prior schedules (e.g., adjustment toschedule data 170408). In other example embodiments, the neural network170426 may be trained on a training data set based at least in part onsuccessful adjustments to prior schedules (e.g., adjustment to scheduledata 170408). As explained above, the adjustments to prior schedules maybe based on schedules from across multiple entities.

Further, in example embodiments, the method 170300 intelligentlyidentifies the second schedule parameter 170414. The method 170300includes intelligently assigning the identified schedule adjustmentbased at least in part on determining that a particular businessscenario exists based at least in part on the schedule data 170408. Inother words, matching the second schedule parameter 170414 to one of theplurality of first schedule parameters 170418 is performedintelligently. One of the intelligent methods may include usingartificial intelligence or machine learning methods to identify thematching. In example embodiments, intelligence may include focusing onone or more business scenarios such as risk of employee turnover,decrease in production, loss of profits, employee dissatisfaction, andthe like. When a user selects a specific business scenario, more focusmay be given to the plurality of first schedule parameters 170418associated with the business scenario to be matched with the secondschedule parameter 170414.

In example embodiments, the adjustments to schedule data 170408 includeseveral operations 170428. The operations 170428 to shifts of a schedulemay include one or more of moving a shift, adding a shift, deleting ashift, and dividing a shift into two or more new shifts. Further, theoperations 170428 to personnel/employees in a schedule may also includeone or more of moving an employee to another shift, adding an employeeto a shift, or deleting an assigned employee from a shift. Also,adjustments to schedule data 170408 may include adjusting a bias of aconnector 170430 in an agglomerate network. Therefore, operationsperformed herein may influence the operations of the agglomerate networkat large.

Referring to FIG. 166 , an agglomerate network 170500 for generatingschedule data 170504 may be provided. The agglomerate network 170500 forgenerating schedule data may include a scheduler circuit 110502structured to output the schedule data 170504, a connector circuit170506 structured to adjust at least one of an input to the schedulercircuit 170502 or the schedule data 170504 outputted by the schedulercircuit 170502, and a schedule spreader circuit 170508. The schedulespreader circuit 170508 may be structured to maintain 170510 a list ofrecommended schedule adjustments 170512. Each recommended scheduleadjustment may correspond to one of a plurality of first scheduleparameters 170514. Further, the agglomerate network may analyze 170516the schedule data 170504 to identify a second schedule parameter 170518.In embodiments, the agglomerate network 170500 may also includeidentifying a recommended schedule adjustment 170520 from the list170512 via matching the second schedule parameter 170518 to one of theplurality of first schedule parameters 170514 corresponding to therecommended schedule adjustment. Responsive to the identifiedrecommended schedule adjustment 170520, the agglomerate network 170500may further generate a spread command value 170522 structured to triggera change to the schedule data 170504. In embodiments, the change to theschedule data 170504 includes adjusting the connector circuit 170506according to the recommended schedule adjustment. Further, theagglomerate network 170500 may include transmitting the spread commandvalue 170524.

Referring to FIG. 167 , certain further aspects of the agglomeratenetwork 170500 for generating schedule data are described following, anyone or more of which may be present in certain embodiments. Inembodiments, the agglomerate network 170500 may provide for building170602 the list of recommended schedule adjustments 170512. The list ofrecommended schedule adjustments 170512 may include schedule adjustmentsthat successfully improved prior schedules possessing a plurality ofthird schedule parameters 170604 corresponding to the plurality of firstschedule parameters 170514. The improvement may be with respect to thesecond schedule parameter 170518. In embodiments, the list ofrecommended schedule adjustments 170512 includes prior scheduleadjustments across multiple entities. For example, the scheduleadjustment may further adjust a bias of a connector 170606 in theagglomerate network.

One non-limiting use case of schedule spreading may be where anapparatus for schedule spreading detects a change in a schedule forcompany A, where the change was extending a Friday night shift from five(5) hours to eight (8) hours but removing a Monday shift for the samework week. The apparatus may detect that company B is similar to companyA and either recommends and/or implement a similar change to a schedulefor company B. As will be appreciated, generally, the moreentities/companies the apparatus and methods disclosed herein haveaccess to, the higher the number of changes that can be propagatedand/or the higher the likelihood that a particular change that isspread, e.g., recommended and/or implemented, to an entity/corporationwill have meaningful benefits, e.g., detectable changes in one or moreobjectives, as disclosed herein. In other words, the larger the pool ofentities/corporations the more likely and closely companies can bematched and, therefore, the higher the likelihood that a change willhave a significant benefit to an entity that it is spread to. Inembodiments, the number of entities that the apparatuses and/or methodsdisclosed herein may have access to the schedule data of may be on theorder of hundreds, thousands, tens-of-thousands, hundreds-of-thousands,millions, tens-of-millions, etc. As will be appreciated, in embodiments,automated detection of matching entities and schedule data changes, asdisclosed herein, makes it practical to search through the schedule datafor a large number of entities, e.g., hundreds, thousands,tens-of-thousands, hundreds-of-thousands, millions, tens-of-millions,etc., to find suitable changes for spreading.

Embodiments of schedule spreading, as disclosed herein, may provide forthe spreading of schedules, and/or portions thereof, between entitiesthat do not and/or cannot normally share data and/or converse with eachother, e.g., corporations on opposites sides of the globe and/or indifferent industries. For example, the apparatus 170100 (FIG. 162 )and/or an agglomerate network that incorporates the same may be operatedby a third party that services two distinct entities, e.g.,corporations, wherein the schedule interpretation circuit 170102 hasaccess to schedule data corresponding to a first of the two entities,and the adjusts schedule data corresponding to a second of the twoentities based on the schedule data corresponding to the first of thetwo entities. By spreading schedule data between such entities,embodiments of the current disclosure provide for the spreading ofbeneficial scheduling approaches that would likely not happen in theabsence of the apparatuses and methods disclosed herein. As will beunderstood, however, embodiments of the current disclosure may requireapproval and/or the consent of an entity before spreading its scheduledata, as disclosed herein. In embodiments, an entity may be able tolimit which other entities its schedule data may be spread to.

As described herein, embodiments of the current disclosure provide forapproaches and architectures for chaining two or more agglomeratenetwork modules/circuits together via connectors, e.g., module/component130 (FIG. 1 ). The chained agglomerate network modules/circuits mayinclude: schedulers, weather modules, retention modules, sales/profitmodules, and/or any other type of models/modules used in businessforecasting and/or described herein. As also disclosed herein,connectors may serve as the links/data tunnels that pass data betweenagglomerate network modules/circuits and/or manipulate inputs and/oroutputs of agglomerate network modules/circuits.

In embodiments, connectors may bias results. For example, a connectormay receive as a first input, an output from a scheduler circuit thatdoes not account for weather data, and the same connector may receive asa second input, an output from a weather module/circuit. In the eventthe weather module circuit predicts a high likelihood of snow, theconnector may bias the first input, e.g., the output of the scheduler,such that the resulting biased data decreases the number of employeesscheduled during the expected snowfall. Conversely, if the weathermodule/circuit predicts a high likelihood of sunny weather, theconnector may bias the first input, e.g., the output of the scheduler,such that the resulting biased data increases the number of employeescheduled during the sunny weather. In embodiments, connectors may beused to bias the output of a first agglomerate network circuit/module tomake the outputted data usable by other agglomerate networkcircuits/modules.

In embodiments, connectors may detect and/or correct out-of-boundsresults. For example, a first agglomerate network circuit/module mayoutput data known to be excessive and/or a defect of the firstagglomerate network circuit/module, e.g., a predicted sales volume fortires that exceeds the world's rubber supply. A connector may detectthat the data received from the first agglomerate network circuit/moduleis outside of acceptable and/or logical values and bias the data tobring it back to be within acceptable values.

Embodiments of connectors may weigh multiple agglomerate networkmodules/circuits. For example, a connector may receive inputs from fouragglomerate network models/circuits and pass one of the inputs, as anoutput, to another agglomerate network module/circuit and/or connector.In such embodiments, the passed input may be the one that scores thehighest and/or is otherwise determined by the connector to be the mostsuitable to pass on. Accordingly, embodiments of the connectors mayscore inputs received from upstream agglomerate network circuits/modulesand pass on the highest scoring input. In embodiments, connectors maypass on a subset of the received inputs, e.g., the three highest scoringinputs and/or any input scoring above a threshold. In embodiments,connectors may pass the received inputs through a function and pass onthe result to downstream agglomerate network circuits and/or connectors.For example, connectors may pass on the average of received inputs.

Embodiments of connectors may determine quantity requirements fornon-continuous schedules. Embodiments of connectors may also provide formanipulation/constraining of outputs.

Embodiments of connectors may pass confidence(s), as described herein,through an agglomerate network. Connectors may pass and/or addagglomerate network circuit outputs and/or confidence (corresponding tothe scheduling data) to a data array that pairs agglomerate networkcircuit outputs to confidences. In embodiments, the data array mayrecord the history of scheduling data as it passes between agglomeratenetwork circuits/modules and/or connectors. For example, a connector mayreceive the data array from an upstream connector where the last entryand/or row in the data array includes the output of the most recentupstream agglomerate network circuit/module and its correspondingconfidence value. The connector may then send the most recent entry inthe data array to a subsequent agglomerate network circuit forprocessing, wherein the connector may receive the output of thesubsequent agglomerate network circuit and store it as a new row(optionally with a corresponding confidence) in the data array and thenpass the data array on to a downstream connector.

Connectors may be of the following levels: Level 1, a connector thatprovides a confidence value for a single result and/or may use historicdata and/or AI training to generate the result; Level 2, a connectorthat may use confidence bands, e.g., a learned curve, and/or split bellcurves; Level 3, a connector that incorporates external module factorsthat would be inefficient and/or impossible to practically incorporateinto an agglomerate network circuit/module, e.g., the age of afranchise, accounting for a recent a change in managers, macroeconomicconditions, weather events, special events, and/or the appearance of anemployee on another schedule; Level 4, a connector that provides forshifting of values, e.g., biasing, determination of when connectoradjustments are out of norms and the like.

In embodiments, the connectors may provide for theincorporation/consideration of factors/variables not natively consideredby the agglomerated modules/circuits without the need to rewrite theagglomerate network modules/circuits, e.g., a scheduling module thatdoes not account for weather data may have its output biased by aconnector that does account for weather data. Embodiments may useextrapolation techniques that impact confidence as they are passedthrough an agglomerate network. Embodiments may use machine learning toadjust one or more aspects of a connector, e.g., its bias(es), inputs,and/or outputs.

Accordingly, referring to FIG. 168 , an apparatus 230100 in accordancewith embodiments of the current disclosure is shown. The apparatus230100 may be a single computing device and/or form part of one or moreother computing devices disclosed herein. The apparatus 230100 mayinclude a plurality of agglomerate network circuits 230110, 230112,230114, 230116, 230110, 230112, 230114, 230116, and a plurality ofconnector circuits 230118, 230120, 230122. The plurality of agglomeratenetwork circuits 230110, 230112, 230114, 230116, may each be structuredto interpret input data 230124 and transmit output data 230126. Theplurality of connector circuits 230118, 230120, 230122 may be eachstructured to: interpret the output data 230126 of a correspondingagglomerate network and execute a connector action 230128 based at leastin part on the interpreted output data 230126. In embodiments, theconnector action 230128 performed by at least one of the connectorcircuits, e.g., 230118, may propagate 230130 the output data 230126 of afirst agglomerate network circuit, e.g., 230110, as the input data230124 of a second agglomerate network circuit, e.g., 230112. Inembodiments, the connector action 230128 performed by at least one ofthe connector circuits, e.g., 230120, may bias 230132 the output data230126 of an agglomerate network circuit, e.g., 230112. In embodiments,the connector action 230128 performed by at least one of the connectorcircuits, e.g., 230120, may realign 230134 the output data 230126 of anagglomerate network circuit, e.g., 230112, to be within an acceptableand/or predetermined range. In embodiments, the connector action 230128performed by at least one of the connector circuits, e.g., 230122, mayweigh 23036 the outputs 230126 of at least two agglomerate networkcircuits, e.g., 230114 and 120116. In embodiments, the connector action230128 performed by at least one of the connector circuits, e.g.,230120, may propagate 230138 a confidence value 230140, corresponding tothe output data 230126 generated by a first agglomerate network circuit,e.g., 230112, to a second agglomerate network circuit, e.g., 230116,with the corresponding output data 230126.

In embodiments, the confidence value 230140 may be generated via machinelearning, and/or be based at least in part on historic data. Inembodiments, confidence value 230140 may be generated based at least inpart on a curve. In embodiments, curve may be a bell curve and/or split.In embodiments, at least one of the plurality of agglomerate networkcircuits/modules 230110, 230112, 230114, 230116 is and/or includes ascheduler module, a weather model, a retention model, a sales model, aprofit model, and/or any other type of model described herein and/orthat would be useful for generating a schedule. In embodiments, at leastone of the connector circuits 230118, 230120, 230122 receives input fromtwo agglomerate network circuits/models and propagates output data230124 that is based at least in part on both inputs. In embodiments, atleast one of the connector circuits receives input from a singleagglomerate network circuit/module and propagates the output data to twoagglomerate network circuits/modules. In embodiments, at least one ofthe connector circuits propagates output data to a same agglomeratenetwork circuit/model from which it receives input. In embodiments, atleast one of the connector circuits propagates the output data toanother of the connector circuits.

Illustrated in FIG. 169 is a method 230200, in accordance withembodiments of the current disclosure. The method 230200 may beperformed via apparatus 230100 (FIG. 168 ) and/or any computing devicedisclosed herein. The method 230200 includes generating schedule datavia a plurality of agglomerate network circuits 230210. Each of theagglomerate network circuits may be structured to interpret input dataand generate output data based at least in part in the interpreted inputdata. The method 230200 further includes executing a plurality ofconnector actions via one or more connector circuits 230212. Theplurality of connector actions may effect generation of the scheduledata and include at least one of: propagating the output data of a firstagglomerate network circuit of the plurality as the input data of asecond agglomerate network circuits of the plurality 230214, biasing theoutput data of an agglomerate network circuit of the plurality 230216,realigning the output data of an agglomerate network circuit of theplurality to be within an acceptable range 230218, weighting the outputsof at least two agglomerate network circuits of the plurality 230220, orpropagating a confidence value, corresponding to the output datagenerated by a first agglomerate network circuit of the plurality, to asecond agglomerate network circuit of the plurality with thecorresponding output data 230222. The method 230200 further includestransmitting the schedule data 230224.

Embodiments of the current disclosure may also provide for anon-transitory computer-readable medium storing instructions that adaptat least one processor to generate schedule data via a plurality ofmodels/modules. Each of the models/modules may be structured to:interpret input data, and generate output data based at least in part onthe interpreted input data. The stored instructions may further adaptthe at least one processor to execute a plurality of connector actionsvia one or more connector circuits. At least one of the connectoractions of the plurality at least one of: propagates the output data ofa model of the plurality as the input data of a second model of theplurality, biases the output data of a model of the plurality, realignsthe output data of a model of the plurality to be within an acceptablerange, weights the outputs of at least two models of the plurality, orpropagates a confidence value, corresponding to the output datagenerated by a first model of the plurality, to a second model of theplurality with the corresponding output data. The stored instructionsfurther adapt the at least one processor to transmit the schedule data.

Shown in FIG. 170 is another apparatus 230300, in accordance withembodiments of the current disclosure. The apparatus 230300 may be asingle computing device and/or form part of one or more other computingdevices disclosed herein. The apparatus 230300 includes a plurality ofagglomerate network circuits 230310, 230312, 230314, 230316, and aplurality of connector circuits 230318, 230320, 230322. The plurality ofagglomerate network circuits 230310, 230312, 230314, 230316 isstructured to generate schedule data 230324. The plurality of connectorcircuits 230318, 230320, 230322 is structured to propagate data 230326between each of the plurality of agglomerate network circuits 230310,230312, 230314, 230316. The plurality of agglomerate network circuits230310, 230312, 230314, 230316 includes a scheduler; and at least one ofthe plurality of connector circuits 230318, 230320, 230322 is structuredto adjust at least one of input data 210328 to at least one of theplurality of agglomerate network circuits or output data 210330 from theat least one of the plurality of agglomerate network circuits.

Referring now to FIG. 171 , embodiments of the system, as disclosedherein, may use a rule-length-encoded (RLE) representation of one ormore schedules. For example, a one-week schedule is depicted in FIG. 171which encodes scheduled shifts as a start and end time (Note that lunchbreaks split some shifts). In the depicted representation of FIG. 171 ,color represents which job the employee is scheduled to work, and thisrepresentation corresponds to ID shown in table 1 below which lists asampling of possible data input representations. As can be seen, four(4) variations can be composed by selecting either one-hot orinteger-valued encoding, and either weekly or day-of-week encoding. Theexample column refers to one hypothetical Cash Register department froma retail store in the mid-Atlantic region, using 3- and one-half yearsof weekly schedules for training to generate a schedule for one week inJune.

TABLE 1 ID Name Axes Value Examples 1 Fine-grained Employee (integer)vs. Integer Dimensions: 31 GANTT job Day of Week (DOW) representing(emp) X 7 assignment - (integer) vs. 15-minute the job (DOW) × 96(intervals). DOW variant interval. The channel assignment, Please seedimension is shared with 0 Explanation of the with the employee denotingno channel dimension dimension, but could assignment of for other layersbe its own separate the employee dimension. to any job (In some runs thejob integers were normalized to [0,1]; in others they were normalized to[−1,−1]). 2 Fine GANTT Employee (integer) vs. Value is Dimensions: 31job assignment - DOW (integer) vs. 15- binary (0/1 (emp) X 7 DOW one-minute interval vs job. or −0.5/0.5), (DOW) × 96 hot encoded The channeldimension indicating at (intervals) × 10 variant is shared with the eachmatrix (9 jobs, employee dimension, position and one for but could beits own whether there “no job”). separate dimension. was an assignmentof that employee to that job at that 15- minute slot. 3 Fine-grainedEmployee (integer) vs. Same as Dimensions: 31 GANTT job 15-minuteinterval over ID 1 (emp) × 672 assignment - the week. The channel(intervals). Note week variant dimension is shared that convolution withthe employee here should take dimension, but could into account the beits own separate weekly periodicity dimension. by using strides of 7. Ican provide examples from our runs if that helps. 4 Fine-grainedEmployee (integer) vs. Same as Dimensions: 31 GANTT job DOW (integer)vs. 15- ID 2 (emp) × 672 assignment - minute interval over the(intervals) ) × 10 (9 week one-hot week vs job. The jobs, and one forvariant channel dimension is “no job”). shared with the employeedimension, but could be its own separate dimension.

In embodiments, the generator networks may reverse-mirror the criticnetworks in each case, except, in some embodiments, for the final criticlayer, which may be just one unit, whereas the initial generator layermay be a noise vector. As will be understood, other similar GANTT-stylerepresentations of input and labor forecasts may be used. For example,availability of employee preferences and forecasts may be time-basedrepresentations. In embodiments, availability axes may be employee vs.time vs. availability level (e.g., preferred available, unavailable,unknown, . . . ); forecast axes may be at least job/task vs. time vs.labor demand. Another example for a labor forecast that uses the15-minute time granularity may be “Customer Service at the Waltham storeneeds 3 employees to work between 10:15 AM and 10:30 AM on 8/9/2021”.

The inputs representations of Table 1, along with the correspondingGANTT-style representations of availability and forecast, may be thesole inputs to the discriminator (and also the representation of thegenerated schedule).

As will be understood, the channel dimension referenced in Table 1 maybe from and/or based, in part, on the vision domain, e.g., such asvision generative adversarial networks (GANs) where an image has achannel dimension which represents the color, e.g., a dimension of sizeone (1) for black and white images or of size three (3) for RGB colorimages. For example, the input dimensions of a 64 pixel×64 pixel RGBcolor image are 64×64×3. The critic (a.k.a. discriminator) network maystart with activations that represent the 64×64×3 image and pass thoseactivations through successive layers until they reach a single 1×1 unitwhose activation is the probability that the image is a fake. An exampleof the dimensions of successive layers is64×64×3—>32×32×128—>16×16×256—>8×8×512—>4×4×1024—>1×1×1.

In embodiments, the channel dimension starts as a representation of animage's colors, but in intermediate layers it may allow the criticnetwork to control the degree of generalization. The image dimensions inone layer and its successor may be determined by the filter, e.g., aconvolution filter, and, as such, may be set fixed to some extent. Note,however, the critic may need to have flexibility, because too many unitsmay allow memorization of the real images, and/or too few units may nothave the capacity to hold the latent representation. In embodiments, thechannel dimension may serve the purpose of allowing a number of units ateach layer that is appropriate for the layer's degree of generalization.Channel dimensions in schedule GANs may serve the same purpose. Anon-limiting example is shown in Table 2, in which one run(corresponding to ID 1 in Table 2) has an input representation of7×96×31 (DOW×interval×employee), where the employee dimension alsoserves as the channel dimension. The layer progression in the critic isshown in table 2.

TABLE 2 Employee/ Kernel Stride DOW Interval channel applied to appliedto dimen- dimen- dimen- (DOW, (DOW, Unit Layer sion sion sion Int.)Int.) count Input 7 96 31 (1, 2) (2, 2) 20832 Hidden 4 48 62 (2, 5) (2,5) 11904 1 Hidden 2 9 124 (2, 5) (2, 5) 2232 2 Output 1 1 1 N.A. N.A. 1

Referring to FIGS. 172-176 , as another example, for each GAN networkarchitecture, the progression in the number (or relative fraction) ofunits (“volume”) may be tailored to match GAN networks known to besuccessful. The “Radford” and “Toy MNIST” schemes are two suchsuccessful networks. As will be appreciated, FIG. 173 depicts an examplediscriminator architecture determination table, FIG. 174 depicts anexample generator architecture determination table; and FIGS. 175 and176 depict example discriminator architecture determination graphs. FIG.172 depicts the architecture of the convolutional neural network asdetermined by scheme 672-0 shown in FIG. 174 , in accordance withembodiments of the current disclosure.

Referring to FIG. 177 , an apparatus 260100 to improve networkperformance is depicted, e.g., a resolution determiner, which may formpart of component/module 128 and/or 132 (FIG. 1 ). The apparatus 260100includes an historic data processing circuit 260102 to interprethistoric schedule performance data 260110 and a network architectureprocessing circuit 260104 to interpret network architecture data 260124,where the network architecture data 260124 defines, in part, a networkproperty 260130 of an agglomerate network 260112. The apparatus 260100further includes a resolution analysis circuit 260108 to generate anadjustment command value 260128. The adjustment command value 260128 isbased, at least in part, on the historic schedule performance data260110, or the network architecture data 260124. The adjustment commandvalue 260128 is structured to affect an adjustment to the agglomeratenetwork 260112 or to a network property 260130 of the agglomeratenetwork 260112 to improve the performance metric 260122. An adjustmentprovisioning circuit 2601308 transmits the adjustment command value260128.

The agglomerate network 260112 includes a plurality of agglomeratenetwork circuits 260114 and a plurality of connector circuits 260118where the connector circuits 260118 network architecture data 260124 mayinclude structural relationships between various agglomerate networkcircuits 260114 and connector circuits 260118. Although FIG. 177 depictsa single connector circuit 260118 connecting two agglomerate networkcircuits 260114 this should not be considered limiting. Otherconfigurations such as multiple connector circuits 260118 connected to acommon network circuit 260114, a single connector circuit 260118connecting to multiple (more than the two shown) network circuits260114, and the like are contemplated. The network architecture data260124 may include information describing relationships between theplurality of network circuits 260114 and the plurality of connectorcircuits 260118. The network architecture data 260124 may includeparameters and properties of individual network circuits 260114 orconnector circuits 260118. Properties of an individual network circuit260114 may include the type of network circuit 260114, a resolution of anetwork circuit 260114, connections of the individual network circuit260114, and the like. Properties of a connector circuit 260118 mayinclude types of connection supported, processing of transferred data,and the like. The network architecture data 260124 may include a numberof network circuits 260114 and/or a number of connector circuits 260118in the agglomerate network 260112.

The adjustment command value 260128 may include an adjustment to one ormore of the structural relationships between a network circuit 260114and a connector circuit 260118. The adjustment command value 260128 mayinclude an adjustment to a type of network circuit 260114 or a type ofconnector circuit 260118. The adjustment may correspond to switching toa component having different properties, such as switching a currentnetwork circuit 260114 to a network circuit 270114 having higher orlower resolution than the current network circuit 260114. The adjustmentcommand value 260128 may include a change (either an increase ordecrease) to the number of network circuits 060114 or the number ofconnector circuits 260118 that includes the agglomerate network 260112.The adjustment command value 260128 may correspond to a number of cyclesexecuted by one of the plurality of agglomerate network circuits 260114.The adjustment command value 260128 may be a message to be displayed toa user. The adjustment command value 260128 may be structured todirectly execute the adjustment, where the adjustment is a directadjustment to the network architecture data 260124, a direct adjustmentto the agglomerate network 260112, and the like.

Referring to FIG. 178 , an example of an agglomerate network 260102 forgenerating schedule data is depicted. The agglomerate network 260102includes a plurality of network circuits 260114 structured to generateschedule data 260120 and a plurality of connector circuits 260118structured to propagate schedule data 260120 between various networkcircuits 260114. The agglomerate network 260102 may further include aresolution determiner circuit 260208. The resolution determiner circuit260208 may interpret historic performance data 260212 (which may includehistoric schedule data 260202 and/or historic performance metrics260204) and network architecture data 260124 and generate, based on theinterpretation, an adjustment command value 260128. The resolutiondeterminer circuit 260208 then transmits the adjustment command value260128 where the adjustment command value 260128 is structured to makean adjustment to the agglomerate network 260102 to improve a performancemetric 260218.

A performance metric may correspond to a level of employee compliance(how well was the schedule followed), a number of sales, a level ofshift production, a rate of shift production, a number of sales, anamount of profit, an amount of positive feedback and the like.

In some embodiments, the resolution determiner circuit 260208 may alsoinclude an experimentation circuit 260210 structured to performexperiments on the agglomerate network 260102 or on an agglomeratenetwork model 260222. Experiments may include simulating: an insertionor deletion of a network circuit 260114, an insertion or deletion of aconnector circuit 260118, a change in a structural relationship betweenone of more of the network circuits 260114 and connector circuits260118, and the like. Experiments may be performed sequentially or inparallel. The experimentation circuit may calculate performance metricsbased on outcomes of the different experiments. The adjustment commandvalue 260128 may be based, at least in part, on the experimentaloutcomes of the different simulations.

Referring to FIG. 179 , a method 260300 for improving the performance ofan agglomerate network is depicted. The method 260300 may includeinterpreting historic schedule performance data 260302, and interpretingnetwork architecture data 260304. Historic schedule performance data mayinclude schedule data, inputs for a given scheduling scenario, aconfiguration of the agglomerate network used, the precited results,and/or actual results (how the corresponding schedule actuallyperformed), and corresponding performance metrics. Network architecturedata may include one or more properties of the agglomerate network. Themethod 260300 may further include generating an adjustment command value260308 and transmitting the adjustment command value 260310. Theadjustment command value may be based on historic schedule performancedata and network architecture data and may be structured to effect: analert to a user, an adjustment to the agglomerate network, an adjustmentto network architecture data, and the like as discussed elsewhereherein.

Referring to FIG. 180 , an example of processor actions 260400 resultingfrom instructions stored on a non-transitory computer-readable mediumare depicted. The processor actions 260400 may include interpreting260402 historical schedule performance data. The historical scheduleperformance data may include: schedule data that was generated by anagglomerate network of network circuits and connector circuits, and oneor more performance metrics resulting from the schedule data. Theprocessor actions 260400 may further include interpreting 260404 networkarchitecture data where the network architecture data defines, in part,a property of the agglomerate network. The processor actions 260400 mayfurther include generating 260408 an adjustment command value andtransmitting 260410 the adjustment command value. The adjustment commandvalue is generated, based at least in part, on the historical scheduleperformance data and the network architecture data. The adjustmentcommand value may be structured to affect a chance to the agglomeratenetwork to improve a performance metric of the agglomerate network.

In embodiments, scheduling modules, e.g., module/component 126 (FIG. 1), may each generate a plurality of initial schedules. The schedules maythen be evaluated and/or propagated in an agglomerate network todetermine which is the best schedule or to determine a schedule thatmeets desired criteria. The number and/or type of schedules generated byeach module may be fixed and/or may be dynamically or algorithmicallydetermined. In some embodiments, the modules may be configured togenerate schedules that provide a variety of different schedules or atop number of schedules. In embodiments, the scheduling modules may beconfigured to generate a threshold number of schedules based on previousselections of schedules. The threshold number may be determined by firstgenerating a first number of schedules, for example, ten (10) schedules,and then identifying which of the first number of schedules is selectedby the agglomerate network. If the selected schedule were at a firstthreshold number of the generated schedules, for example, if theselected schedule were the ninth or tenth generated schedule, e.g., outof the ten schedules, the threshold number of schedules may be increasedto a second threshold number of generated schedules, for example,fifteen (15) schedules. If the selected schedule were the first of thesecond threshold number of generated schedules, the threshold number ofschedules may be decreased, for example, to 5 schedules. The process ofadjusting the threshold number of generated schedules may beperiodically or continuously adjusted to reduce unnecessarycomputations, while still providing enough schedules to find a suitableschedule.

Referring to FIG. 181 , an apparatus 300100 may be provided. Theapparatus 300100 includes a schedule generation circuit 300102 and athreshold number determining circuit 300104. The threshold numberdetermining circuit 300104 includes a first schedule evaluation circuit300106, including a first evaluation circuit 300108 and a firstthreshold determining circuit 300110, and a second schedule evaluationcircuit 300112, including a second evaluation circuit 300114 and asecond threshold determining circuit 300116.

The schedule generation circuit 300102 is structured to generate aplurality of initial schedules 300118 for an agglomerate network, theplurality of initial schedules 300118 including a first number ofschedules 300120. The threshold number determining circuit 300104 isstructured to determine a threshold number 300122 of schedules.

The first evaluation circuit 300108 is structured to evaluate theplurality of initial schedules 300118 to select a first schedule 300124for the agglomerate network, among the plurality of initial schedules300118, that meets desired criteria 300126. The first thresholddetermining circuit 300110 is structured to identify a first placenumber 300128, among the first number of schedules 300120, of theselected first schedule 300124, and if the first place number 300128 isgreater than or equal to a first threshold number 300130 of thegenerated schedules, the first threshold number 300130 being within twoof the threshold number 300122: increase the threshold number 300122 ofschedules to a second threshold number 300132 of generated schedules,greater than the first threshold number 300130, and instruct theschedule generation circuit 300102 to generate a second plurality ofschedules 300134 equal to the second threshold number 300132 ofschedules.

The second evaluation circuit 300114 is structured to evaluate thesecond plurality of schedules 300134 to select a second schedule 300136for the agglomerate network, among the second plurality of schedules300134, that meets the desired criteria 300126. The second thresholddetermining circuit 300116 is structured to identify a second placenumber 300138, among the second plurality of schedules 300134, of theselected second schedule 300136, and if the second place number 300138is ‘1’ or ‘2’, decrease the threshold number 300122 of schedules to athird threshold number 300140 of schedules, less than the firstthreshold number 300130.

Referring to FIG. 182 , certain further aspects of the apparatus 300100are described following, any one or more of which may be present incertain embodiments. In certain embodiments, the schedule generationcircuit 300102 may be further structured to propagate the plurality ofinitial schedules 300118 in the agglomerate network 300202. In certainembodiments, the schedule generation circuit 300102 may be furtherstructured to propagate the second plurality of schedules in theagglomerate network 300202. In certain embodiments, the selected firstschedule 300124 may be determined to be a best schedule 300204 among theplurality of initial schedules 300118 based on a best fit to the desiredcriteria 300126, and the selected second schedule 300136 may bedetermined to be a best schedule 300204 among the second plurality ofschedules 300134 based on a best fit to the desired criteria 300126. Incertain embodiments, at least one of a number or a type 300206 ofschedule generated by the schedule generation circuit 300102 may befixed or may be dynamically or algorithmically determined. In certainembodiments, the schedule generation circuit 300102 may be furtherstructured to generate a variety of different schedules 300208.

In certain embodiments, the schedule generation circuit 300102 may befurther structured to generate a top number 300210 of schedules. Incertain embodiments, the first number of schedules 300120 may bedetermined based on previous selections of schedules 300212. In certainembodiments, the first threshold number 300130 may be 10. In certainembodiments, the second threshold number 300132 may be 15. In certainembodiments, the third threshold number 300140 may be 5. In certainembodiments, the threshold number determining circuit 300104 may befurther structured to determine the threshold number 300122 of schedulesperiodically or continuously.

In certain embodiments, the schedule generation circuit 300102 may befurther structured to rank 300214 each of: the plurality of initialschedules 300118, and the second plurality of schedules 300134. Incertain embodiments, the ranking 300214 may be based on at least one ofa top scoring 300216 or a diversity rating 300218. In certainembodiments, a bias 300220 may be applied to the ranking 300214.

In certain embodiments, the schedule generation circuit 300102 may befurther structured to generate a third plurality of schedules 300222equal to the third threshold number 300140 of schedules, the thirdplurality of schedules 300222 including a first of the third thresholdnumber 300140 of the second plurality of schedules 300134. In certainembodiments, the schedule generation circuit 300102 may be furtherstructured to generate the plurality of initial schedules 300118 and thesecond plurality of schedules 300134 based at least in part on machinelearning 300224.

Referring to FIG. 183 , a method 300300 may be provided. The method300300 includes generating a plurality of initial schedules for anagglomerate network, the plurality of initial schedules including afirst number of schedules 300302, and determining a threshold number ofschedules 300304, including: evaluating the plurality of initialschedules to select a first schedule for the agglomerate network, amongthe plurality of initial schedules, that meets desired criteria 300306,identifying a first place number, among the first number of schedules,of the selected first schedule 300308, if the first place number isgreater than or equal to a first threshold number of the generatedschedules, the first threshold number being within two of the thresholdnumber 300310: increasing the threshold number of schedules to a secondthreshold number of generated schedules, greater than the firstthreshold number 300312, and generating a second plurality of schedulesequal to the second threshold number of schedules 300314, evaluating thesecond plurality of schedules to select a second schedule for theagglomerate network, among the second plurality of schedules, that meetsthe desired criteria 300316, identifying a second place number, amongthe second plurality of schedules, of the selected second schedule300318, and if the second place number is ‘1’ or ‘2’, decreasing thethreshold number of schedules to a third threshold number of schedules,less than the first threshold number 300320.

Referring to FIGS. 184-185 , certain further aspects of the method300300 are described following, any one or more of which may be presentin certain embodiments. In certain embodiments, the method 300300 mayfurther include propagating the plurality of initial schedules in theagglomerate network 300402. In certain embodiments, the method 300300may further include propagating the second plurality of schedules in theagglomerate network 300404. In certain embodiments, the selected firstschedule may be determined to be a best schedule among the plurality ofinitial schedules based on a best fit to the desired criteria, and theselected second schedule may be determined to be a best schedule amongthe second plurality of schedules based on a best fit to the desiredcriteria 300406. In certain embodiments, at least one of a number or atype of generated schedule may be fixed or may be dynamically oralgorithmically determined 300408. In certain embodiments, the method300300 may further include generating a variety of different schedules300410. In certain embodiments, the method 300300 may further includegenerating a top number of schedules 300412.

In certain embodiments, the method 300300 may further includedetermining the first number of schedules based on previous selectionsof schedules 300502. In certain embodiments, the first threshold numbermay be ten (10). In certain embodiments, the second threshold number maybe fifteen (15). In certain embodiments, the third threshold number maybe five (5). In certain embodiments, the method 300300 may furtherinclude determining the threshold number of schedules periodically orcontinuously 300504.

In certain embodiments, the method 300300 may further include rankingeach of: the plurality of initial schedules, and the second plurality ofschedules 300506. In certain embodiments, the ranking may be based on atleast one of a top scoring or a diversity rating. In certainembodiments, a bias may be applied to the ranking.

In certain embodiments, the method 300300 may further include generatinga third plurality of schedules equal to the third threshold number ofschedules, the third plurality of schedules including a first of thethird threshold number of the second plurality of schedules 300508. Incertain embodiments, the method 300300 may further include generatingthe plurality of initial schedules and the second plurality of schedulesbased at least in part on machine learning 300510.

Referring to FIG. 186 , a non-transitory computer-readable medium 300600may be provided. The non-transitory computer-readable medium 300600stores instructions that adapt at least one processor to: generate aplurality of initial schedules for an agglomerate network, the pluralityof initial schedules including a first number of schedules 300602, anddetermine a threshold number of schedules 300604, including instructionsto: evaluate the plurality of initial schedules to select a firstschedule for the agglomerate network, among the plurality of initialschedules, that meets desired criteria 300606, identify a first placenumber, among the first number of schedules, of the selected firstschedule 300608, if the first place number is greater than or equal to afirst threshold number of the generated schedules, the first thresholdnumber being within two of the threshold number 300610: increase thethreshold number of schedules to a second threshold number of generatedschedules, greater than the first threshold number 300612, and generatea second plurality of schedules equal to the second threshold number ofschedules 300614, evaluate the second plurality of schedules to select asecond schedule for the agglomerate network, among the second pluralityof schedules, that meets the desired criteria 300616, identify a secondplace number, among the second plurality of schedules, of the selectedsecond schedule 300618, and if the second place number is ‘1’ or ‘2’,decrease the threshold number of schedules to a third threshold numberof schedules, less than the first threshold number 300620.

Referring to FIG. 187 , certain further aspects of the non-transitorycomputer-readable medium 300600 are described following, any one or moreof which may be present in certain embodiments. In certain embodiments,the non-transitory computer-readable medium 300600 may further includeinstructions that adapt the at least one processor to propagate theplurality of initial schedules in the agglomerate network 300702. Incertain embodiments, the non-transitory computer-readable medium 300600may further include instructions that adapt the at least one processorto propagate the second plurality of schedules in the agglomeratenetwork 300704. In certain embodiments, the non-transitorycomputer-readable medium 300600 may further include instructions thatadapt the at least one processor to generate a top number of schedules300706. In certain embodiments, the non-transitory computer-readablemedium 300600 may further include instructions that adapt the at leastone processor to determine the first number of schedules based onprevious selections of schedules 300708.

The methods and systems, e.g., the platform 100 (FIG. 1 ) describedherein may be deployed in part or in whole through a machine having acomputer, computing device, processor, circuit, and/or server thatexecutes computer readable instructions, program codes, instructions,and/or includes hardware configured to functionally execute one or moreoperations of the methods and systems herein. The terms computer,computing device, processor, circuit, and/or server, (“computingdevice”) as utilized herein, should be understood broadly.

An example computing device includes a computer of any type, capable toaccess instructions stored in communication thereto such as upon anon-transient computer readable medium, whereupon the computer performsoperations of the computing device upon executing the instructions,e.g., executes and/or configures an agglomerate network as describedherein. In certain embodiments, such instructions themselves includes acomputing device. Additionally or alternatively, a computing device maybe a separate hardware device, one or more computing resourcesdistributed across hardware devices, and/or may include such aspects aslogical circuits, embedded circuits, sensors, actuators, input and/oroutput devices, network and/or communication resources, memory resourcesof any type, processing resources of any type, and/or hardware devicesconfigured to be responsive to determined conditions to functionallyexecute one or more operations of systems and methods herein.

Network and/or communication resources include, without limitation,local area network, wide area network, wireless, internet, or any otherknown communication resources and protocols. Example and non-limitinghardware and/or computing devices include, without limitation, ageneral-purpose computer, a server, an embedded computer, a mobiledevice, a virtual machine, and/or an emulated computing device. Acomputing device may be a distributed resource included as an aspect ofseveral devices, included as an interoperable set of resources toperform described functions of the computing device, such that thedistributed resources function together to perform the operations of thecomputing device. In certain embodiments, each computing device may beon separate hardware, and/or one or more hardware devices may includeaspects of more than one computing device, for example as separatelyexecutable instructions stored on the device, and/or as logicallypartitioned aspects of a set of executable instructions, with someaspects including a part of one of a first computing device, and someaspects including a part of another of the computing devices.

A computing device may be part of a server, client, networkinfrastructure, mobile computing platform, stationary computingplatform, or other computing platform. A processor may be any kind ofcomputational or processing device capable of executing programinstructions, codes, binary instructions and the like. The processor maybe or include a signal processor, digital processor, embedded processor,microprocessor or any variant such as a co-processor (math co-processor,graphic co-processor, communication co-processor and the like) and thelike that may directly or indirectly facilitate execution of programcode or program instructions stored thereon. In addition, the processormay enable execution of multiple programs, threads, and codes. Thethreads may be executed simultaneously to enhance the performance of theprocessor and to facilitate simultaneous operations of the application.By way of implementation, methods, program codes, program instructionsand the like described herein may be implemented in one or more threads.The thread may spawn other threads that may have assigned prioritiesassociated with them; the processor may execute these threads based onpriority or any other order based on instructions provided in theprogram code. The processor may include memory that stores methods,codes, instructions and programs as described herein and elsewhere. Theprocessor may access a storage medium through an interface that maystore methods, codes, and instructions as described herein andelsewhere. The storage medium associated with the processor for storingmethods, programs, codes, program instructions or other type ofinstructions capable of being executed by the computing or processingdevice may include but may not be limited to one or more of a CD-ROM,DVD, memory, hard disk, flash drive, RAM, ROM, cache and the like.

A processor may include one or more cores that may enhance speed andperformance of a multiprocessor. In embodiments, the process may be adual core processor, quad core processors, other chip-levelmultiprocessor and the like that combine two or more independent cores(called a die).

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer readable instructions ona server, client, firewall, gateway, hub, router, or other such computerand/or networking hardware. The computer readable instructions may beassociated with a server that may include a file server, print server,domain server, internet server, intranet server and other variants suchas secondary server, host server, distributed server and the like. Theserver may include one or more of memories, processors, computerreadable transitory and/or non-transitory media, storage media, ports(physical and virtual), communication devices, and interfaces capable ofaccessing other servers, clients, machines, and devices through a wiredor a wireless medium, and the like. The methods, programs, or codes asdescribed herein and elsewhere may be executed by the server. Inaddition, other devices required for execution of methods as describedin this application may be considered as a part of the infrastructureassociated with the server.

The server may provide an interface to other devices including, withoutlimitation, clients, other servers, printers, database servers, printservers, file servers, communication servers, distributed servers, andthe like. Additionally, this coupling and/or connection may facilitateremote execution of instructions across the network. The networking ofsome or all of these devices may facilitate parallel processing ofprogram code, instructions, and/or programs at one or more locationswithout deviating from the scope of the disclosure. In addition, all thedevices attached to the server through an interface may include at leastone storage medium capable of storing methods, program code,instructions, and/or programs. A central repository may provide programinstructions to be executed on different devices. In thisimplementation, the remote repository may act as a storage medium formethods, program code, instructions, and/or programs.

The methods, program code, instructions, and/or programs may beassociated with a client that may include a file client, print client,domain client, internet client, intranet client and other variants suchas secondary client, host client, distributed client and the like. Theclient may include one or more of memories, processors, computerreadable transitory and/or non-transitory media, storage media, ports(physical and virtual), communication devices, and interfaces capable ofaccessing other clients, servers, machines, and devices through a wiredor a wireless medium, and the like. The methods, program code,instructions, and/or programs as described herein and elsewhere may beexecuted by the client. In addition, other devices required forexecution of methods as described in this application may be consideredas a part of the infrastructure associated with the client.

The client may provide an interface to other devices including, withoutlimitation, servers, other clients, printers, database servers, printservers, file servers, communication servers, distributed servers, andthe like. Additionally, this coupling and/or connection may facilitateremote execution of methods, program code, instructions, and/or programsacross the network. The networking of some or all of these devices mayfacilitate parallel processing of methods, program code, instructions,and/or programs at one or more locations without deviating from thescope of the disclosure. In addition, all the devices attached to theclient through an interface may include at least one storage mediumcapable of storing methods, program code, instructions, and/or programs.A central repository may provide program instructions to be executed ondifferent devices. In this implementation, the remote repository may actas a storage medium for methods, program code, instructions, and/orprograms.

The methods and systems described herein may be deployed in part or inwhole through network infrastructures. The network infrastructure mayinclude elements such as computing devices, servers, routers, hubs,firewalls, clients, personal computers, communication devices, routingdevices and other active and passive devices, modules, and/or componentsas known in the art. The computing and/or non-computing device(s)associated with the network infrastructure may include, apart from othercomponents, a storage medium such as flash memory, buffer, stack, RAM,ROM and the like. The methods, program code, instructions, and/orprograms described herein and elsewhere may be executed by one or moreof the network infrastructural elements.

The methods, program code, instructions, and/or programs describedherein and elsewhere may be implemented on a cellular network havingmultiple cells. The cellular network may either be frequency divisionmultiple access (FDMA) network or code division multiple access (CDMA)network. The cellular network may include mobile devices, cell sites,base stations, repeaters, antennas, towers, and the like.

The methods, program code, instructions, and/or programs describedherein and elsewhere may be implemented on or through mobile devices.The mobile devices may include navigation devices, cell phones, mobilephones, mobile personal digital assistants, laptops, palmtops, netbooks,pagers, electronic books readers, music players and the like. Thesedevices may include, apart from other components, a storage medium suchas a flash memory, buffer, RAM, ROM and one or more computing devices.The computing devices associated with mobile devices may be enabled toexecute methods, program code, instructions, and/or programs storedthereon. Alternatively, the mobile devices may be configured to executeinstructions in collaboration with other devices. The mobile devices maycommunicate with base stations interfaced with servers and configured toexecute methods, program code, instructions, and/or programs. The mobiledevices may communicate on a peer-to-peer network, mesh network, orother communications network. The methods, program code, instructions,and/or programs may be stored on the storage medium associated with theserver and executed by a computing device embedded within the server.The base station may include a computing device and a storage medium.The storage device may store methods, program code, instructions, and/orprograms executed by the computing devices associated with the basestation.

The methods, program code, instructions, and/or programs may be storedand/or accessed on machine readable transitory and/or non-transitorymedia that may include: computer components, devices, and recordingmedia that retain digital data used for computing for some interval oftime; semiconductor storage known as random access memory (RAM); massstorage typically for more permanent storage, such as optical discs,forms of magnetic storage like hard disks, tapes, drums, cards and othertypes; processor registers, cache memory, volatile memory, non-volatilememory; optical storage such as CD, DVD; removable media such as flashmemory, e.g., USB sticks or keys), floppy disks, magnetic tape, papertape, punch cards, standalone RAM disks, Zip drives, removable massstorage, off-line, and the like; other computer memory such as dynamicmemory, static memory, read/write storage, mutable storage, read only,random access, sequential access, location addressable, fileaddressable, content addressable, network attached storage, storage areanetwork, bar codes, magnetic ink, and the like.

Certain operations described herein include interpreting, receiving,and/or determining one or more values, parameters, inputs, data, orother information (“receiving data”). Operations to receive datainclude, without limitation: receiving data via a user input; receivingdata over a network of any type; reading a data value from a memorylocation in communication with the receiving device; utilizing a defaultvalue as a received data value; estimating, calculating, or deriving adata value based on other information available to the receiving device;and/or updating any of these in response to a later received data value.In certain embodiments, a data value may be received by a firstoperation, and later updated by a second operation, as part of thereceiving a data value. For example, when communications are down,intermittent, or interrupted, a first receiving operation may beperformed, and when communications are restored an updated receivingoperation may be performed.

Certain logical groupings of operations herein, for example methods orprocedures of the current disclosure, are provided to illustrate aspectsof the present disclosure. Operations described herein are schematicallydescribed and/or depicted, and operations may be combined, divided,re-ordered, added, or removed in a manner consistent with the disclosureherein. It is understood that the context of an operational descriptionmay require an ordering for one or more operations, and/or an order forone or more operations may be explicitly disclosed, but the order ofoperations should be understood broadly, where any equivalent groupingof operations to provide an equivalent outcome of operations isspecifically contemplated herein. For example, if a value is used in oneoperational step, the determining of the value may be required beforethat operational step in certain contexts (e.g., where the time delay ofdata for an operation to achieve a certain effect is important), but maynot be required before that operation step in other contexts (e.g.,where usage of the value from a previous execution cycle of theoperations would be sufficient for those purposes). Accordingly, incertain embodiments an order of operations and grouping of operations asdescribed is explicitly contemplated herein, and in certain embodimentsre-ordering, subdivision, and/or different grouping of operations isexplicitly contemplated herein.

The methods and systems described herein may transform physical and/oror intangible items from one state to another. The methods and systemsdescribed herein may also transform data representing physical and/orintangible items from one state to another.

The methods and/or processes described above, and steps thereof, may berealized in hardware, program code, instructions, and/or programs or anycombination of hardware and methods, program code, instructions, and/orprograms suitable for a particular application. The hardware may includea dedicated computing device or specific computing device, a particularaspect or component of a specific computing device, and/or anarrangement of hardware components and/or logical circuits to performone or more of the operations of a method and/or system. The processesmay be realized in one or more microprocessors, microcontrollers,embedded microcontrollers, programmable digital signal processors orother programmable device, along with internal and/or external memory.The processes may also, or instead, be embodied in an applicationspecific integrated circuit, a programmable gate array, programmablearray logic, or any other device or combination of devices that may beconfigured to process electronic signals. It will further be appreciatedthat one or more of the processes may be realized as a computerexecutable code capable of being executed on a machine readable medium.

The computer executable code may be created using a structuredprogramming language such as C, an object oriented programming languagesuch as C++, or any other high-level or low-level programming language(including assembly languages, hardware description languages, anddatabase programming languages and technologies) that may be stored,compiled or interpreted to run on one of the above devices, as well asheterogeneous combinations of processors, processor architectures, orcombinations of different hardware and computer readable instructions,or any other machine capable of executing program instructions.

Thus, in one aspect, each method described above, and combinationsthereof, may be embodied in computer executable code that, whenexecuting on one or more computing devices, performs the steps thereof.In another aspect, the methods may be embodied in systems that performthe steps thereof, and may be distributed across devices in a number ofways, or all of the functionality may be integrated into a dedicated,standalone device or other hardware. In another aspect, the means forperforming the steps associated with the processes described above mayinclude any of the hardware and/or computer readable instructionsdescribed above. All such permutations and combinations are intended tofall within the scope of the present disclosure.

While the disclosure has been disclosed in connection with certainembodiments shown and described in detail, various modifications andimprovements thereon will become readily apparent to those skilled inthe art. Accordingly, the spirit and scope of the present disclosure isnot to be limited by the foregoing examples but is to be understood inthe broadest sense allowable by law.

What is claimed is:
 1. An apparatus comprising: a sequencing circuit structured to interpret sequence data corresponding to a sequence designed, in part, by a user; and a mimicking circuit structured to: extract a sequence trend from the sequence data; identify a portion of the sequence data corresponding to the extracted sequence trend; and generate new sequence data based at least in part on the identified portion; and a sequence data provisioning circuit structured to transmit the sequence data.
 2. The apparatus of claim 1, wherein the user is a manager of a unit to which the sequence data corresponds.
 3. The apparatus of claim 1, wherein the user is a manager of a first unit distinct from a second unit, and the sequence data corresponds to the second unit.
 4. The apparatus of claim 3, wherein the first unit and the second unit are in a same organization.
 5. The apparatus of claim 3, wherein the first unit and the second unit are in distinct organizations.
 6. The apparatus of claim 1, wherein the extraction of the sequence trend is based at least in part on machine learning.
 7. The apparatus of claim 6, wherein the machine learning involves a neural network trained to match a portion of the sequence data with an event, and generation of the sequence data is based at least in part on an association of the matched portion and the sequence trend.
 8. The apparatus of claim 7, wherein the event is a holiday.
 9. The apparatus of claim 7, wherein the event is inclement weather.
 10. The apparatus of claim 7, wherein the event is an equipment failure.
 11. The apparatus of claim 7, wherein the event is a shortage of materials or product for sale.
 12. The apparatus of claim 1, wherein a mimic command value is structured to adjust a connector circuit of an agglomerate network.
 13. The apparatus of claim 12, wherein the adjustment to the connector circuit is structured to set or change a bias of the connector circuit.
 14. A method comprising: determining, via a sequence interpretation circuit, sequence data corresponding to a sequence designed, in part, by a user; extracting, via a mimicking circuit, a sequence trend from the sequence data; identifying, via the mimicking circuit, a portion of the sequence data corresponding to the extracted sequence trend; generating, via the mimicking circuit, new sequence data based at least in part on the identified portion; and transmitting, via a sequence data provisioning circuit, the new sequence data.
 15. The method of claim 14, wherein the user is a manager of a first unit distinct from a second unit, and the sequence data corresponds to the second unit.
 16. The method of claim 14, wherein the extraction of the sequence trend is based at least in part on machine learning.
 17. The method of claim 16 further comprising: generating, via a mitigation circuit and based at least in part on the sequence data and austere event data, a mitigation action command value structured to trigger an adjustment to the sequence data, wherein the adjustment is structured to effect a change of a property of the sequence data to mitigate an effect of an austere event corresponding to the austere event data on one or more entities associated with the sequence data; and transmitting, via a mitigation action provisioning circuit, the mitigation action command value.
 18. An agglomerate network for generating sequence data, the agglomerate network comprising: a sequencer circuit structured to output the sequence data; a connector circuit structured to adjust at least one of an input to the sequencer circuit or the sequence data outputted by the sequencer circuit; and a sequence mimicker circuit structured to: interpret historical sequence data; extract a sequence trend from the historical sequence data; identify a portion of the sequence data corresponding to the extracted sequence trend; generate a mimic command value based at least in part on the identified portion, wherein the mimic command value is structured to trigger an adjustment to the connector circuit, wherein the adjustment is structured to effect a change of at least one of the input to the sequencer circuit or the sequence data outputted by the sequencer circuit; and transmit the mimic command value.
 19. The agglomerate network of claim 18, wherein the mimic command value is structured to adjust the connector circuit of the agglomerate network.
 20. The agglomerate network of claim 19, wherein the adjustment to the connector circuit is structured to set or change a bias of the connector circuit. 